We provide both a PDF and a text-only version. For the PDF version, see the bottom of this page. Below is the text-only version.

Street Seen: A Surveillance Walking Tour of Pittsburgh

Image: A map of Pittsburgh, Pennsylvania, USA that shows the neighborhoods around the intersection of the Allegheny and Monongahela rivers. Neighborhoods like the downtown business district, Mount Washington, East Allegheny, Troy Hill, and Elliott are included. A marker highlights Nova Place, a building in Allegheny Center. 

What is This?

STREET SEEN is an exploration of surveillance and smart city gentrification, organized by critical technology collective coveillance and artists Wesleigh Gates and Maggie Oates.

Last summer, we invited community members, artists, and activists to join us for a walking tour of Northside’s tech hub, Nova Place. Through communal knowledge sharing and group activities, the tour aimed to build collective power and reclaim the legibility of urban infrastructures.

This zine documents our journey and some of the questions and activities we engaged with. We hope it will prompt folks to learn more, or even serve as inspiration for your own actions.

Why is This?

Why did we make this project?

Pittsburgh’s urban laboratory agenda has caused rapid, disruptive change to the fabric of the city, giving private players space to reshape the city while often leaving residents out of the conversation.

Resistance to gentrifying technologies in Pittsburgh has been enacted through strategies such as a community campaign against the autonomous Mon-Oakland shuttle corridor, calls by Black activists to curb law enforcement’s use of surveillance tech, and demands for accountability on university complicity in creating these infrastructures.

This project is meant to highlight these forms of resistance, make visible the ways tech alters space and infrastructure, and foster dialogue around everyday urban spaces as sites of solidarity.

1. Place

Nova Place is a self-described “innovation space” on Pittsburgh’s North Side. Home to offices, restaurants, a co-working space, and a gym, it also hosts a large number of tech companies and data centers.

This site has a long and complex history. A bustling civic center in the late 19th and early 20th centuries, the area later “declined” (thanks to discriminatory zoning practices) and was razed in the 1960s to make space for a new mall, displacing hundreds of residents. The mall was briefly successful before most of its stores closed by the ‘90s. NYC-based Faros Properties bought the space in 2013 to develop Nova Place.

Nova is just one part of a century-long lineage of spatial discrimination against this community’s Black and low-income residents.

Image: A photo of the front of Sealy’s, a store that was inside the mall that is now Nova Place. The aesthetics of the store (faded pastel colors, patterned wallpaper, color block rectangles, and retro signs) suggest the photo might have been taken in the 1970-80s. 

Exercise

Look around the plaza. How do you feel here? Comfortable, uncomfortable? Safe? Anxious? Make a pose that expresses that feeling.

Now imagine your idea of the perfect use of this space. Imagine who else is here, and what’s happening. Shift your pose to show how you feel inside this ideal space.

Big or small, let the pose start to move through your whole body. Let the movement start to travel through the plaza.

As you move, look around at the others in the group. Keep moving as we witness each other and all the ways we could inhabit this space.

2. Infrastructure

There are lots of places like Nova — anonymous-looking complexes incorporating residential and commercial space. It seems pretty boring, but look closely. Some communication infrastructure wants to be seen, and some doesn’t. Some surveillance cameras are obvious, as a warning to those passing by. Other infrastructure is stashed away because it’s considered ugly. And some is hidden for protection, from the weather or from “bad actors” or from curious/concerned citizens like us.

Image: A simple, almost silly hand-drawing of a person saying, “HELLO DATA ARE YOU THERE?” while looking down at a “manhole” cover labeled “BELL.” The curious person leans down with their hands on each hip. 

Exercise

What parts of the communication network can you see from where you’re standing? Do you notice any sensors? Any mysterious boxes? Can you guess where anything is hidden underground or in a wall?

With a partner or a small group, chat about what you see (or don’t see). What purposes are these infrastructures serving? What else might they be collecting, and who is that data useful to?

Some things we saw…

Shotspotter

This is a device designed to detect gunfire and give a heads up to the police. Pittsburgh introduced them in 2014 in “crime-ridden East End neighborhoods,” and they have since expanded across the city, including the North Side. We don’t know exactly what they look like because the company and the city are both cagey about it, but they might look like the mysterious object on the streetlight above…

Image: A hand-drawn street light pole stretches high on the page. On top of the light is a white box with rounded corners and a small black arm, presumably a Shotspotter. 

Sensus FlexNet utility box

This is a smart meter that attaches to your gas, water, or electric lines. It collects data about usage and can also help turn your water service off. These are nice because a person no longer has to come to your house to read the meter. But it also means utility companies have a lot of information about you. Nationally, many utility companies sell usage & payment information to data companies, who package it up with other personal information and in turn sell that to law and immigration enforcement agencies. 

Image: A hand-drawn sensor box is bolted to a brick wall. The sensor is tan, around the size of 2 fists. It has rounded corners, textured waves on the front, and is embossed on the front face with the word “SENSUS.” The front face has a circle inset around the size of a quarter. You might be able to identify this sensor by touch. 

Bell manhole cover

In the 80s, AT&T was an absurdly large monopoly, so it was ordered to split into 7 “Baby Bell” companies, including Bell Atlantic, which in present day is owned by Verizon. Under our feet is a thick bunch of cables, likely owned mostly by Verizon. If you have Verizon Fios in your home or work, most websites you visit run into this building under a manhole like this one. 30+ fiber optic companies operate in and out of this building.

Doodle Time

Doodle 1

Find the nearest utility markings near you. Here’s a guide of what the markings mean:

Red: Electric, Cables, Conduit, Lighting Cables

Orange: Communication, Phone, Cable, Fiber Optic

Yellow: Natural Gas, Oil, Steam, Petroleum

Green: Sewer

Blue: Drinking water

Purple: Reclaimed water, Irrigation

Pink: Temporary markings, Unknown

White: Proposed digging

Image: An empty box intended for drawing. 

Non-visual alternative activity: Utilities are sometimes marked in soft ground with small vinyl flags instead of paint. The flags are usually 4-5 inches (10-13cm) on a 1ft (31cm) wire pole. Can you find any?

Doodle 2

Draw a diagram of your neighborhood as you understand it now. Draw a diagram of it as it used to be at any point in time. What’s changed? What buildings are new? Is there new infrastructure, new housing? How is that affecting the ways you interact with your communities? 

Image: An empty box intended for drawing. 

Non-visual alternative activity: Visualize or make a recording of this prompt.

Open up these pages for a diagram of our walking tour!

Interlude: Map Centerfold

Image: An overhead map of Nova Place with 4 markers of stops for the walking tour. Nova Place is a building complex and pedestrian area enclosed on all 4 sides by South, West, East, and North Commons streets. The main Nova Place building is a large multi-story rectangle spanning the entire block. It has two towers. Just north of Nova Place is Buhl Park, a small green space with a sidewalk running diagonally through the center. To the west of the park is a Park View Residential building, a multi-story housing complex. North of Park View is a children’s museum, with a dome roof and a playspace out front. Walking due east from the children’s museum is New Hazlett Theater and then Four Allegheny Center. If you turned north, you’d walk between two more tall Park View apartment buildings. 

Marker #1: PLACE. This marker is represented by an upside down thumbtack at the southwest corner of Buhl Park. A hand-drawn cartoon head with dark cropped hair and a beard says, “As a kid, I remember walking here and there was a little apple tree.” The interviewer asks, “Did you eat them?” And he replies, “No! You threw them!” A cartoon crabapple flies by.

Path from #1 to #2: Walk south on the sidewalk that follows the west side of Nova Place, along Children’s Way. Pass the large metal mist sculpture. Stop at the curve in the sidewalk where it turns to the west. 

Marker #2: INFRASTRUCTURE. This marker is represented by a tin can with a string coming out of one end. Image: A photo from one of the walking tours. A group of 7 people gather at the curve of a sidewalk, with their backs to a brick wall. 6 are adults, 1 is a teenager looking down seriously at a paper guide. The group is a mix of race and gender. On the street in front of them is a manhole cover. One White person with fluffy white hair and overalls smiles as they hand binoculars to a smiling Black man in basketball attire. 

Path from #2 to #3: Continue west on the sidewalk along Children’s Way. Follow the exterior of Nova Place, taking the first left to walk south along West Commons. Around halfway down the block there will be a bank storefront in Nova Place, on the left, and shortly after the bank, there will be a few metal benches also on your left. Just past the benches, step into the small grassy area between the building and the sidewalk. Stop when you reach a cluster of several manholes nestled in the grass.

Marker #3: INFORMATION. This marker is represented by a faucet with a single drop of water about to drip out of it. Image: A photo from one of the walking tours, featuring the same 7 people from the previous photo. Four people are standing on manhole covers in the grass, and three are standing on the sidewalk nearby. A tall White person on the sidewalk is reaching across and shaking the hand of a shorter White person who is standing on the nearest manhole cover, while the others look on.

Path from #3 to #4: Continue south in the same direction you were walking, then turn left with the sidewalk to keep Nova Place on your left side. Go straight along South Commons for half a block, crossing the entrance to the Nova Place garage on your left. When you reach the intersection with Federal Street on your right, there will be an entrance to the Nova Place complex on your left. Turn left into the building and go up two sets of stairs. (We are very sorry to report that the last stop on the tour is not wheelchair accessible.) Stop when you’ve reached the interior courtyard at the top of the second set of stairs.

Marker #4: SECURITY. This marker is represented by a standard directional security camera. Image: a goofy hand-drawn cartoon of a person with dark cropped hair and a beard. They are smiling a carefree smile and holding a joint with a trail of smoke emerging from it. In a speech bubble, the person says: “They’re just such a part of life. They don’t bother me none!” An asterisk clarifies that “they” are security cameras.

Final path: To make your way to the central plaza of Nova Place, retrace your steps down only the first set of stairs you previously climbed. On this landing, there will be an alcove to your left: turn and enter the alcove, then pass through the door on your left that leads into the Nova Place parking garage. Walk straight through this large and likely fairly empty space until you reach an elevator. Take the elevator to the Lobby level. Exit the elevator into a small glass vestibule, then exit the vestibule into the main plaza of Nova Place. You’ve reached the end of the tour!

3. Information

We’re always giving off information: through our actions and behavior, our choices and decisions, the places we go and the people we spend time with. Not to mention the purchases we make, the websites we visit, the pictures we post…

Many groups are interested in gathering all of this information. Advertisers, corporations, political campaigns, law enforcement, and more. Often the data collected by one of these entities are shared with or sold to others, as we saw with the smart utility boxes. So even when we think we know who we’re sharing something with, chances are it’s going somewhere else too.

All these aspects of our lives and our selves are constantly flowing through the fiber optic cables beneath our feet, off to destinations and purposes unknown.

Image: A simple hand-drawing of a manhole cover. The center of the cover is embossed with the outline of a bell and labeled with “BELL.” The manhole has a non-specific embossed pattern. You might be able to identify this object by touch. 

Exercise

Notice the five manhole covers on the ground in this area. Each is labeled with the name of the company that owns the fiber optic cable running beneath it. Let’s get five volunteers to represent these companies — each of you stand on top of the manhole cover that belongs to your company.

Now for the rest of us, think of a piece of information about yourself. It can be as simple as what you had for breakfast, or maybe it’s the last thing you looked up on your phone, or something you bought with your credit card.

One by one, each of us will introduce ourselves to one of the fiber companies and share this information with them. Fiber companies, as you shake hands with each individual, swing them down the line to the next company. Repeat until each person and piece of information has “passed through” all five networks.

Image: A hand drawing of three communications companies are drawn as boxes with silly heads and bare feet. These companies, Level 3 Comms, Verizon, and Nextlink, look at each other in a circle. Instead of arms, they “hold hands” by connecting to each other with looping wires. 

4. Security

Image: A hand-drawn directional security camera points down from the upper corner of the page. 

If you look in the quiet corners of complexes like Nova, chances are you’ll find a data center. These are big rooms full of servers that are just there to store and share data. Your personal digital files are stored on your home computer on hard drives, or inside your phone in tiny memory cards. But most everything we access online is stored in a data center. When you upload a photo to iCloud or Google Photos or Flickr, that jpg file zings through cables and gets stored in a room like this.

There are at least four commercial data centers at Nova Place. One is run by Expedient, and on their webpage they list “Video Camera Surveillance” as a selling point. Sure enough, the area around Expedient’s unmarked door at Nova is practically crawling with security cameras. Data centers are often pretty low-profile or even secretive, due to security concerns.

Image: A hand-drawn person peeks out from behind a pillar at the bottom of the page. The person’s long straight hair, cautious surprised face, and one black boot are visible.

Exercise

Look around and identify every camera you can find in this space. If you feel comfortable, walk and stand directly underneath one so that we can all see clearly where it is.

Take a moment and feel yourself being seen by the camera(s). How does it feel in your body — tight, tense? Vulnerable, exposed? Or maybe it feels so ordinary it doesn’t even register?

Image: a fisheye security camera leans out from one side of the page.

Now find a spot where you are visible to the fewest possible cameras… or even none. Take a few deep breaths, loosen your muscles, relax your body. Do you feel any different?

Image: A hand-drawn person with a medium afro hides behind a trash can, out of sight of cameras. They are seated on the ground, one leg curled up and one leg poking out from beneath the can. Their face seems cautious or surprised, but not scared. 

Let’s have a moment of celebrating our bodily autonomy together, with a communal dance party! You can perform for the cameras or return to your hidden space, whichever feels better. Our bodies are our own, no matter how many cameras are pointed at us or how many data centers have bits of our selves cached away in their vaults. Move in whatever way feels good to you, to celebrate your body and to feel the joy of reclaiming this space together.

Let’s take this outside.

Though our tour takes place at Nova Place, there’s much we learned about the transformation of space and infrastructure brought by tech development. We see multiple parallels with many sites around PIttsburgh being prepared for tech development.

Here are a few places worth continuing the work that other community organizers and residents have been doing and continuing the investigating of tech infrastructure in the city:

Image: A map of Pittsburgh, Pennsylvania, USA that shows the neighborhoods around the intersection of the Allegheny and Monongahela rivers. The following sites are highlighted in the map:

• Almono site in Hazelwood (Mon-Oakland Connector campaign)
• Bakery Square and East Liberty (Penn Plaza and Stop the Station campaign)
• Lexington Technology Park in Point Breeze
• Pittsburgh Innovation District in Oakland (Ongoing CMU and Pitt conflicts)

Links to PDFs

• Screen version: download here
• Print version: download here

Embedded PDF (screen reading)

• Read / download PDF

Note: this guide is a work in progress and may change at any time! We’ve done our best to cite our sources, but this page has not been professionally fact-checked.

This workshop was first run as part of two pilot workshops with the Tech Equity Coalition, in partnership with the ACLU of Washington, in October 2019. A zine based on this work was included at the CtrlZ.AI zine fair and the HOT MESS digital exhibition in 2020.

Introduction

In this tour of downtown Seattle, we’ll practice spotting some of the layers of the “smart” city that are hidden in plain sight, collecting and storing data about our lives, as well as the kinds of thinking that justify their existence. Each surveillance technology in our field guide includes the following categories to help you “spot” surveillance technology in the wild: Address, Appearance, What it does, How the tech works, Social importance, Discussion and finally, References.

Tour route

This is the route we will be taking on the walking tour. Click on each stop to pop up its location, and feel free to explore it on Google Maps, e.g. with Street View. The route spans 1.3 miles. Below, we outline each of the surveillance tools/sites listed above.

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Surveillance cameras

Address: Practically everywhere, but the above example is at 523 Union St.

Appearance: Poles, ledges, overhangs, rooftops. They are often spotted watching parking lots, doors, banks, intersections, and government buildings. Indoors, they are typically spotted on roofs and near cash registers.

What it does: The camera has a memory. It can record video or other data and add it to a store of records over all time. The camera can be controlled remotely: it can swivel, zoom, or change height.

How the tech works: Camera recordings can be analyzed for patterns and shared with other entities, both private (your neighbors) and public (the local police).

It might be connected to a network (via Internet or radio frequency), which lets it send video to anywhere, receive instructions from anywhere, and lets other people, who might be anywhere, watch the video stream.

Social importance:  The camera can have different ways of seeing encoded in it, including kinds of gazes that enforce social agreements about what kinds of behavior and people are considered “normal” and these gazes can be propagated all at once to the whole network of enforcement that the camera hooks into.

Discussion

• What are other ways to question the need to have cameras, or surveillance, at all? What sort of society would we build around this way of life?
• What are your individual or communal experiences of “light shining more brightly on some than others”?
• What if each camera were replaced by a person? How would that change how you feel?

References

• Street-level surveillance overview (EFF)
• Video surveillance system overview (ACLU)
• What’s wrong with public video surveillance? (ACLU)

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Amazon Go

Address: 2131 7th Ave

Appearance: Looks like it could be any other convenience store… but it’s not! Inside, you must scan an app to enter, and there are no cashiers.

What it does: Amazon Go tracks your movement using overhead cameras to determine your browsing habits.

How the tech works: Amazon can use your purchases to know more about you using patterns. For example, if you buy Hanukkah decorations, they might know you’re Jewish. Or certain foods might be correlated with certain health issues. They can combine your in-store purchases with your online Amazon purchases for even more predicting power.

Social importance: Patterns can be harmful! They can reinforce stereotypes. For example, Google Photos labeled photos of Black people as “gorilla.”

What is Amazon doing with their knowledge about you? There’s no oversight or transparency. Your data could be sold to third parties without your consent.

Discussion

• What are the societal effects of targeting ads based on [race, gender]?
• When you go into Amazon Go, what do you imagine you are consenting to? How does this differ from the reality? How could this be changed?

References

• Facial recognition used at a convenience store in Seattle (another story)
• How much is your data worth? “No cash needed at this cafe. Students pay the tab with their personal data.”

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Automated license plate reader

Address: 699 Spring Street

Appearance: An automated license plate reader (ALPR) is a little camera that is either mounted to a pole (stationary) in high-traffic locations or the top of a police car (mobile) (Fig. 2.).

What it does: An ALPR photographs the license plate of every car that passes by and records the time and place of the encounter, as well as the plate number (Fig. 3.), and sends the information to a central storing place (called a database). Based on the information from an ALPR (e.g. “plate number ABC1234 detected at the intersection of Pike and Pine at 1:20 PM”), and the type of ALPR, a particular city agency may take an action.

There are three main kinds of ALPRs in Seattle. Stationary ones (type #1), owned by the Dept. of Transportation, are used for traffic purposes, to estimate travel time. Mobile ones, owned by the Seattle Police Dept., are used for parking enforcement (type #2) or law enforcement (type #3), to ping a police officer directly when a “wanted” license plate is spotted. These three kinds of ALPRs have different data retention periods; police ALPR data can be stored for up to 90 days, whereas other ALPR data is (supposedly) deleted immediately. In Seattle, the Seattle Department of Transportation has at least 99 stationary ALPRs deployed, and the Seattle Police Department has 19 vehicles with mounted ALPRs.

How to spot: ALPRs are usually mounted up high near high-traffic areas, like highways, downtown areas, interstates, and bridges. Maps of stationary ones are difficult to find because cities don’t want drivers knowing where they might be issued speeding tickets.

Social importance: Zooming out from an individual ALPR, ALPR systems collect data at a huge volume: the SDOT ALPR system collects information 24/7, resulting in 37,000 license plates in 24 hours, or 13.5 million scans per year. Plates are collected indiscriminately, even from those not suspected of a crime. Nationally, billions of plates are collected nationwide per year.

Regulations on ALPR use—both the technology and the data collected—are mostly nonexistent nationally, as well as in Seattle. That means that the agency that owns the system can choose whether and how they want to retain data, or track vehicle movements. Check out the map in Fig. 4: though SDOT says it does not track individual drivers’ movements, data from an ALPR system could easily be combined to do so.

Because of the lack of regulations, nationally, data-sharing is rampant with license plate data. According to EFF, many law enforcement agencies share plate data directly with each other, even across borders. ALPR data also makes it into private databases such as Thomson Reuters’s CLEAR, access to which can be bought by agencies and private corporations. In Seattle, SDOT and SPD say that they do not share data directly from ALPR systems, but it’s unclear what agencies might be able to access data with a request (per the two Seattle Surveillance Ordinance reports on ALPRs).

When it comes to ALPR data, beware of scope creep (Fig. 5): due to pervasive collection and data-sharing, your license plate could leave its original context and purpose and be used in ways you never consented to, such as private investigation or targeted advertising.

How the tech works: ALPR is one of the older surveillance technologies; it was first invented and tested in the UK in 1984 to detect stolen cars. It uses a technique called optical character recognition (OCR), from a field called computer vision, to automatically make a guess at the letters and numbers in a picture of a license plate. This guess is probabilistic; i.e. it could be wrong. Database technologies allow all the information collected by ALPRs to be collected, and questions asked of it.

Interventions

• In 2015, California and Minnesota passed strict laws placing limits on ALPR data-sharing. Minnesota also bars law enforcement from photographing a vehicle’s occupants. (Source: STOP)

Discussion

• Of the three types of ALPRs, which ones do you think should be used in Seattle?
• Is the convenience of travel time estimates (e.g. WSDOT’s chart) or more efficient law enforcement worth the privacy leaks? What are less-invasive ways that we could achieve the same goals?
• How might ALPR use, and data collection, impact you collectively, as an innocent person who is not directly targeted by the state?

Further questions

• What agencies have access to these systems?
• Which cities are using this or considering?
• What are the WA state rules regarding law enforcement using private sources of ALPR data?
• Which tech companies are providing these systems and how much info do they keep (and what is it used for)?

(We are leaving answers to these questions out of our introductory writeup, but encourage you to find out the answers for your city. Thank you to Tech Fairness Coalition members for asking these questions!)

References

• 2018 Surveillance Impact Report — License Plate Readers — Seattle Department of Transportation
• 2018 Surveillance Impact Report — Automated License Plate Recognition (ALPR) (Patrol) — Seattle Police Department
• 2018 Surveillance Impact Report — Parking Enforcement Systems (Including ALPR) — Seattle Police Department
• EFF — Automated License Plate Readers
• Automated License Plate Readers & Law Enforcement — 2019
• They Are Watching (ACLU) — Automatic License Plate Reader
• “How ICE Picks Its Targets in the Surveillance Age” (The New York Times, 2019)
• “How Britain Exported Next-Generation Surveillance” — on the invention of ALPRs (James Bridle, Medium, 2013)
• Data Driven: What We Learned (EFF study on ALPR data)

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Acyclica

Address: Corners of Spring & 5th and Spring & 4th

Appearance: Flat black circles on top of traffic signal control boxes, which are large, gray or painted metal boxes, typically found at street corners.

What it does: The Acyclica device casts a fake Wi-Fi network and tracks phones that try to join the network in passing cars. Since each phone has a unique identifier (called a MAC address – like your Social Security Number, but for a device), different Acyclica installations can track your personal location as you pass them in the city.

How the tech works:  You know how your phone or laptop auto-connects to Wi-Fi networks?  To do this, your device is shouting to the world a ton of your personal information in something called a probe packet. A probe packet contains the MAC address as well as the list of all the past Wi-fi networks that your device has tried to join before, which can reveal a lot about you! (See Fig. 1.) Acyclica listens for these probe packets, and keeps track of the different places it has heard your MAC address to create a location history.

Social importance: This technology raises many questions around data privacy and consent. Were members of the public asked for consent before the system was installed? No.

Another big issue is data escaping scope. The Seattle city government may promise certain things about the data, but data that govermnment agencies collect historically has a funny way of being stored for longer than promised and shared with other agencies (like ICE or law enforcement) or quasi-private entities (like Palantir) and used to circumscribe the movements of members of marginalized communities.

Discussion

• How do people feel about how Acyclica is collecting their data? What could go wrong? What does the process of coercive data collection “feel” like a mosquito bite? a highway robbery?

References

• Crosscut news article and overview: Seattle’s new technology tracks how we drive
• Seattle Surveillance dossier (large PDF) on Acyclica (see page 8 for a map of Acyclica locations and page 111 for a bunch of valid technical objections)
• Seattle Department of Transportation overview of Acyclica traffic data collection tool

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Washington State Fusion Center

Address: Visit the Washington State Fusion Center (WSFC), in the Abraham Lincoln Building, 1110 3rd Ave, Seattle Washington, 98101

Appearance: Seattle’s fusion center seats a team of 15-30, with full time intelligence officers from the Seattle Police, County Sheriff, state investigators and analysts. These center employees are linked through the State Intelligence Network to every law enforcement agency in the state, and have access to the FBI both through their computer systems as well as through a security corridor linking them to the FBI’s own Field Intelligence Group office on the floor above as well as the Puget Sound Joint Terrorism Task Force.

What it does: After 9/11 fusion centers were born with the “Intelligence Reform and Terrorism Prevention Act of 2004” (IRTPA) along with a host of other “counter-terrorism” intelligence entities such as the Department of Homeland Security. With 18 centers first established, there are now 78 recognized centers. Fusion centers facilitated a national anti-terrorism strategy of intel sharing between local and national agencies as well as with private companies and the military. 

How to spot: This building’s location in downtown Seattle is no accident. Most fusion centers are typically located in urban centers to put them in the center of multiple agencies that administer public safety needs, fire, emergency response, public health providers, and private sector security agencies. 

Social importance:

Multiple incidents of privacy violations and political monitoring are definite examples of concerns associated with fusion centers. But actually as Brendon McQuade argues in “Pacifying the Homeland: Intelligence Fusion and Mass Supervision” this confusing array of coordinating agencies makes it harder to expose political policing the same way as COINTELPRO in the Panther 21 / Handschu Case.

Fusion Centers are mandated to include private sector involvement and their priorities are split between multiple stakeholders at the local, federal, and private level. This puts the role of fusion centers in a fractured light. 

Many fusion centers have played a role in monitoring movements. From the Cato Institute’s summary of ACLU Fusion Center reports, “We’re All Terrorists Now:”

“The North Texas Fusion System labeled Muslim lobbyists as a potential threat; a DHS analyst in Wisconsin thought both pro- and anti-abortion activists were worrisome; a Pennsylvania homeland security contractor watched environmental activists, Tea Party groups, and a Second Amendment rally; the Maryland State Police put anti-death penalty and anti-war activists in a federal terrorism database; a fusion center in Missouri thought that all third-party voters and Ron Paul supporters were a threat; and the Department of Homeland Security described half of the American political spectrum as “right wing extremists.”

However, their role during the Occupy movement showed that many fusion centers claimed official policies of non-involvement in line with DHS’s official policies at the federal level. In cases with private sector stakeholder interests such as in Arizona, however, we see a different story. When Occupy Phoenix targeted the American Legislative Exchange Council (ALEC) for its profit ties to ICE and its role in passing a bill that allowed law enforcement to racially profile latinx drivers, Arizona’s fusion center assigns an officer to monitor Occupy Phoenix and liaise with ALEC. ACTIC Provided ALEC with intelligence, including a “persons of interests” list regarding an protest of an ALEC conference who were later targeted with arrests.

How the tech works:

Fusion centers do not store most of the data available to them. Instead, they negotiate agreements that allow remote access to existing databases. They will work around privacy protections and buy access to the private databases (e.g. Vigilant’s ALPR database; see ALPR walking tour stop), which provide a plethora of information on individuals with no criminal record.

Fusion centers have access to the DHS’s Homeland Security Data network, and several FBI data portals. A few databases used by the WSFC include: 

• Law Enforcement Information Exchange (LINX)
• FBI Systems
• WAFUSION Intake Log
• Regional Information Sharing System Database (RISS)
• Homeland Security State and Local Intelligence Community
• Law Enforcement Online (LEO)
• Washington State Emergency Management Department
• DHS Infrastructure Protection Protective Security Advisor (LENS, IRIS)

Interventions:

What are tools we have against such a large federal, local, and private conglomerate? The strengths of a fusion center also contain its weaknesses. A fractured chain of command often presents conflicts and confusion with rival agency agendas. It appears that some measure of transparency calls and privacy concerns work after major incidents, with some ability to keep watch on the stakeholder agendas that float through fusion center information requests via public records requests. 

Perhaps the greatest effective intervention comes from its funding structure. Though the core “hub” of fusion centers come from federal grants, the specific programs of a fusion center are funded more individually, coming from grants that focus on domains including education, health, and neighborhoods. Such programs promote a model of community wellness that relies on police enforcement. And finally, pre-empting the creation of such centers in the first place might be the most effective intervention with these centers.

Discussion

The location of this fusion center represents a focal point of infrastructure and power. What is being melded together at these fusion centers? Fusion centers popped up in the years after 9/11, particularly from 2003-2007, from an infusion of homeland security grants. This marriage between federal agencies including the CIA, FBI, Homeland Security and other federal bureaus brings a level of national scrutiny to the local level, with individual reporting made possible through the Nationwide Suspicious Activity Reporting (SAR) initiative. This resulted in two European businessmen being reported for “looking suspicious” on the Washington State Ferry in 2007.

Seattle’s most famous case involves the arrest of anti-war Port protestor Phil Chinn, a student at Evergreen State College who was arrested during an anti-war protest in May 2007. The activists had been infiltrated by an army intelligence officer who disseminated protestor information through the fusion center. Not long after the Chinn incident, the center changed its name from Washington Joint Analytical Center (WAJAC) to the Washington State Fusion Center and implemented a number of changes that appear to conform to tighter privacy controls and civil liberties concerns from advocates. 

Fusion Centers play a large role in a “human rights compliant” world of reformed policing. As  Brendon McQuade argues in “Pacifying the Homeland: Intelligence Fusion and Mass Supervision,” the decentralized combination of interests results in a model of “pacification” or control of the economically dis-enfranchised, via a pervasive collection of data to flag and identify locations, communities, and other populations.

We see this in Camden, where a new “reformed” Camden County Police opened a fusion center, the Real Time Tactical Operations Intelligence Center. This center powers a surveillance city, with cameras, ShotSpotter gunshot detectors, automated license plate readers, a mobile observation tower. Community policing interactions become intelligence gathering exercises. All of these data streams flow into Camden’s “fusion center” producing “predictive” analytics to direct police to blocks of interest. Fusion centers exist in a world where surveillance is presented as an alternative model to incarceration and traditional policing. The term used in connection with widespread domestic surveillance – “pacification” – evokes themes of criminalization, gentrification and displacement.

References

• National Network of Fusion Centers Fact Sheet (US Department of Homeland Security site)
• Washington State Fusion Center (official site)
• Washington’s Surveillance Complex (ACLU WA)
• “Watching the Protesters” — on WSFC’s surveillance of Phil Chinn (Seattle Weekly, 2010)
• Pacifying the Homeland: Intelligence Fusion and Mass Supervision (Brendan McQuade)
• Fusion Center Guidelines: Guidelines for Establishing and Operating Fusion Centers at the Local, State, and Federal Levels (US Department of Justice Information Sharing)
• Washington State Fusion Center – Standard Operating Procedures
• Documents on intelligence officer recruitment and training at the Washington State Fusion Center
• 2014 – 2017 National Strategy for the National Network of Fusion Centers 
• Fusion Center Guidelines: Law Enforcement Intelligence, Public Safety, and the Private Sector 
• INFORMATION-SHARING GUIDEBOOK TRANSPORTATION MANAGEMENT CENTERS, EMERGENCY OPERATIONS CENTERS, AND FUSION CENTERS 
• FUSION CENTER SPOTLIGHT 

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AT&T peering site (NSA wiretap site)

Address: 1122 3rd Avenue

Appearance: Tall, windowless building tucked behind a bus stop, with an AT&T logo and sculpture on its front.

What it does: This is a building called a peering site where telecoms (like AT&T) exchange digital information, such as emails, phone calls, and internet chats. Naturally, as a hotspot of information, this is also a good place for intelligence agencies (like the NSA) to wiretap (or eavesdrop on and record) the information.

How to spot: Peering sites are critically important buildings meant for machines, not people. So, they are usually tall, windowless, shored-up buildings near downtown information hotspots, often near law enforcement or intelligence centers.

Social importance: This is just one of the NSA’s eight wiretap rooms in the FAIRVIEW surveillance program (Fig. 3), which allows it to read non-US citizens’ communications with no problem, as well as store US citizens’ communications, which it may read as the law permits (citation needed). AT&T is well-known for its “extreme willingness to help” the NSA.

How the tech works: Because AT&T has such a huge network, it trades network capacity with other Internet service providers, or exchanges it with them. The way that two networks “peer” is that their infrastructure physically meets in a building (as shown in Fig. 4.) to exchange information. 

The NSA is able to eavesdrop on this exchange (through an unknown means) and search through the collected communications.

Discussion

• If you worked at AT&T, what would you do if the NSA asked you to install its wiretapping equipment?
• When it comes to surveillance, should it make a difference whether someone is a US citizen?

References

• “THE WIRETAP ROOMS: The NSA’s Hidden Spy Hubs in Eight U.S. Cities” (The Intercept)
• “TITANPOINTE: The NSA’s Spy Hub in New York, Hidden in Plain Sight (The Intercept)
• “XKEYSCORE: NSA’s Google for the World’s Private Communications” (The Intercept)
• AT&T Helped U.S. Spy on Internet on a Vast Scale (NYT)
• AT&T Global IP Network Peering Policy
• What is peering?

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Discussion

After taking participants through the walking tour, it’s good to bring folks back to a room and have a group discussion of what folks just experienced. Some questions are:

• How can someone “escape the smart city”?
• How is surveillance different in different U.S. cities?
• What are the different layers of surveillance we just discussed? (Help folks distinguish between private, local, state, public, federal, and corporate, and how they interact.)

If you have feedback on this page, or would like to use or adapt the tour in your city, please send us an email.

Note: this guide is a work in progress and may change at any time! We’ve done our best to cite our sources, but this page has not been professionally fact-checked.

This workshop has been run in-person and digitally in a variety of contexts since 2019, including at the CtrlZ.AI zine fair in Barcelona in 2020, online with the Tech Equity Coalition, and in-person at FAccT 2022 in Seoul, South Korea.

Facilitators: 1 to 2, plus 1 per breakout group if conducting digitally
Group size: 5-25
Time: 60-90 minutes
Recommended Location: see In-person vs. Digital section for specifics

Introduction

Surveillance is a complicated topic. We are surveilled by a host of private companies, government agencies at levels from municipal to federal, in physical and digital space, and by a staggering range of technologies.

This workshop has been designed to be useful for individuals who feel overwhelmed by this complexity, and might have questions like “how does this affect me?” or “what should I be most worried about?” It can also be a useful framework for understanding what interventions might be effective in protecting you or your community’s privacy.

The following is a facilitator-facing guide for starting that process. It uses stories and mapping as the primary vehicles for uncovering and categorizing entities and for tracing the flow of information. All of our stories are plausible narratives that we wrote based on actual technologies or events reported by reliable technology journalism organizations. (So, while a story may not have literally happened, it very plausibly could have happened, in our opinion.)

In-person vs. Digital

This workshop has been run successfully both digitally and in-person. Below are material and space requirements for both versions, as well as some tips and tricks.

In-person

In-person versions of this workshop are engaging, and generate conversation and great material evidence for participants and facilitators alike.

Material/space requirements

• Printouts* of map & story, 1 per participant, ideally of the size 11×17″.
• Lots of colorful markers, pens, pencils, etc. At least 5 different colors.
• 1 large format B+W print* of map, 24×36″ or larger. Fedex + UPS stores sell prints of this size for about .
• Screen/projector for presenting slides. To edit the agenda/structure, you can duplicate them (File > Make a copy > Entire presentation)
• Tables + chairs. Ideally small tables to allow for easy break-out groups.

*Contact us if you would like PDFs of these materials.

Digital

This workshop can be engaging and unique in a digital format. We recommend you use Zoom for the workshop itself, or any other video conferencing service that allows breakout rooms (if more than 5 participants) and screen sharing. Unfortunately, while still possible, the act of highlighting the stories and drawing connections is somewhat fiddly. See the Mapping! section for specific advice if conducting this workshop digitally.

Google Slides allows for live editing, which can be used in lieu of highlighting and drawing connections. Here is a link to the slides. You can duplicate the slides (File > Make a copy > Entire presentation) to edit the agenda or structure as you see fit.

Agenda

This agenda is flexible upwards (up to 90 minutes) but trying to fit it into less than an hour means that certain sections will be rushed, or the discussion components will be lost.

• Getting Comfy, Introductions, Acknowledgements: 5-15 minutes (slides 1-2)
• Context + Goals: 5-10 minutes (slides 3-4)
• Map Overview: 5-10 minutes (slides 4-12)
• Mapping Example: 15-20 minutes (slides 13-17)
• Diving In! 20 minutes (slides 18-40)
• Discussion/Conclusion: 10+ minutes (slide 41)

Getting Comfy, Introductions, Acknowledgements

5-15 minutes, slides 1-2

This section is to allow for latecomers and to let everyone get settled.

Introduce yourself, let any other facilitators introduce themselves, and thank the organization/space that is hosting you.

If there is time, let the participants introduce themselves with their names, pronouns, and an answer to an icebreaker question, such as “what has brought you here today?” or “what is the best meal you’ve eaten this week?”

Other ways to use this time are an acknowledgement of current events, a land acknowledgment, or small talk and catching up.

Context + Goals

5-10 minutes, Slides 3-4

• 

We’ve started this section with a quote from Shoshana Zuboff, which we believe is a good framing of why surveillance is such an important topic. Feel free to choose other quotes or pieces of content that you think will be particularly compelling for your audience.

Why Mapping? Why Stories?
You can read this statement word-for-word, or go into more detail by referring to the Introduction section of this page. Further discussions or examples of mapping can be found in the work of William Powhida, The Greater Boston Anti-Displacement Toolkit, and Ed Simon, to name just a few. These links are not necessary for facilitating this workshop, but they do provide some helpful context.

Who’s being watched
This is a segue into the next section. It is also an acknowledgement that surveillance affects us all differently. While not an immediate concern for some privileged individuals (“I have nothing to hide”), surveillance is a force that can further the oppression of marginalized groups.

Map Overview

• 

This section is necessary because the map, just like the surveillance ecosystem, represents an overwhelming amount of content when viewed in its entirety. As a result, the slides are designed to create a build, where you can speak to the various sections that make up the surveillance ecosystem, and by the end have a shared understanding with your participants about the categories and structure of the map.

This section is a good example of how two facilitators are advantageous. By switching off, slide by slide, the content can feel more conversational and less like a lecture.

These categories of the map are as follows:

• Who’s Being Watched (slide 4)
  Surveillance happens to everyone. The data we give up also reveals information about our friends, families, co-workers.
• Watchers (slide 5)
  These can range from government agencies (ICE, NSA) that use surveillance as a key part of their process, well-known technology corporations (Google, Facebook) that make it part of their business model, municipal agencies (local police) that benefit from data sharing, and contractors (Taser, Clarifai) that develop key surveillance technologies.
• Actions (slide 6)
  Shows the wide variety of actions we take in physical and digital space that can be surveilled. Shows both the broad distribution of surveillance tech and which actions are particularly risky.
• Surveillance Technologies (slide 7)
  Hardware and software that collects information about us. Can exist in physical space (surveillance camera) or digital space (browser cookie). Especially nefarious if it exhibits dragnet behavior, collecting information indiscriminately.
• Data (slide 8)
  The information that is collected by surveillance technologies. Can individually identify us (such as our name, biometrics like our face, or our address), or might be aggregated and subject to inference from third parties.
• Databases (slide 9)
  Collections of data held by watchers. Access to these can be sold by data brokers or tech companies, or opened up to government agencies with warrants or subpoenas.
• Algorithmic Systems (slide 10)
  Uses large amounts of potentially biased data to make…
• Inferences (slide 11)
  Guesses made by algorithmic decisions. May or may not be correct but can have…
• Impacts (slide 12)
  Results of the surveillance society + economy on individuals.

Many of these boxes are left open, because it is impossible to map all examples of all of the following. Some are filled in to help the process of mapping, but it is greatly encouraged that participants fill in boxes during the mapping process.

For more information on how to specifically identify or analyze some of these categories, please see the Algorithmic Equity Toolkit.

Mapping Example

Now that the map has been introduced, it is time to give a quick tutorial of the mapping process. Because this is a content-rich workshop, it can be useful to read the room and gauge how much haste and/or participant interaction would be useful at this stage.

Storytelling

Read the story in your best stage voice. Don’t interrupt yourself with commentary, and focus instead on giving it some pacing and intonation that will help the participants simply enjoy the telling. The following story has been annotated with an example of what that might sound like: (italics are emphasis, ellipses “…” show a long pause)

What’s The DiFference?

Click. That’s a good one! Your mom is actually smiling, and your dad didn’t blink. Your sibling looks great, but they have always been photogenic… This has been a great day of good photos, and at the end of it your mom (of course) wants to know where she can see them. Being an amateur professional photographer, you upload all of your best pics to Flickr, but because this is just a hobby you have them accessible on a Creative Commons license…Which is just the license an IBM researcher is building into her web scraping algorithm…With the algorithm, she is able to add a huge amount of images to the Diversity in Faces (DiF) dataset, which is made with the intent to make facial recognition fairer… The picture of your family is annotated, correctly identifying your mom and dad’s age and gender, but enforcing a gender binary onto your sibling… The annotations also identify the skin colors of your family. Using this dataset, which is accessible to public and private research institutions, an applied research firm develops a facial recognition for the NYPD, with the intent of searching for people of a specific age or skin color…

Highlighting

Now you will show the process of breaking the story into the categories that makes up the map. For both Digital and Physical workshops, it will be most visible for you to do this live in Google Slides with the Highlight function:

Choose the color that corresponds to each category. Slide 15 contains our key, but we encourage you to do this process on your own as well. The correct answer is less important than the act of analysis!

If you have enough time, you can explain your rationale for some categorization, or ask briefly participants to volunteer answers for certain sentences:

“There are a couple mappable entries in this sentence. Can I have a volunteer tell me what they think they are?”

Mapping

After the categorization has been done, trace the data flows from component to component through the map. For the components that are missing from the map, fill them in the blank shapes that correspond to that category.

If you are doing this physically, do this with a marker on the large format map. If you are doing this digitally, you can do this on slide 16, using the arrow tool and text box tool in Google Slides.

Move through this section as quickly as possible, as the fun part comes next….

Diving In!

20 minutes, slides 18-40

Based on your group size and format, break the participants into groups of 3-5, Tell them which story they will be working on and when to return back to the main group.

If you are conducting this workshop digitally, it is a good idea to have a pre-chosen facilitator for each working group. Decide which facilitator corresponds to which story beforehand. Here is a guide for those facilitators.

The process for breakout rooms is as follows:

1. Participants introduce themselves briefly.
2. Ask two participants to volunteer as readers.
   If conducting this digitally, the facilitator should share their screen and navigate to the slide that corresponds to the story that group is working on.
3. One reader reads the story from start to end in their best stage voice.
4. Collaboratively, the group decide which components of the stories correspond to which categories on the map. If doing this physically, have them use the different color markers/pens to annotate them. If conducting this digitally, the facilitator should highlight the components in the Google Slides.
5. Following the continuity of the story, trace arrows, using just one color, from component to component in the story, filling in the ones that are missing.
6. At the predetermined time, the groups will come together and share out their results.

When back in the main group, go through the stories in order. Put the story that is being read up on the screen (or screenshare).

For digital workshops: Ask the readers for the group to alternate reading the story out loud, showing the highlighted version and map briefly, and sharing 1-3 insights or questions that came up in the process. Examples can include not being sure how to categorize a story component, noticing a pattern in the data flow through the map, or sharing a (short) related anecdote that they may have come across previously.

For physical workshops: Ask the readers to read the story, and come up to the large map and draw data flows and add missing components, using a different color for each story. Ask them to share out 1-3 insights or questions that came up in the process. Examples can include not being sure how to categorize a story component, noticing a pattern in the data flow through the map, or sharing a (short) related anecdote that they may have come across previously.

Discussion/Conclusion

10+ minutes, slide 41

This section tends to emerge organically from the share-out. The following are four broad questions that we have written, but we invite you to write your own to specify it for your audience or intent.

1. Did the stories fit neatly into the categories?
2. What patterns in data movement did you notice?
3. What stories should we be mapping?
4. How do we stop a data flow?

Appendix I: Stories & Sources

The following is all stories we have currently written and their source(s). All of the components we recommend you map are bold and italic. See the slide deck key for each slide for specific categorization, but please keep in mind that there is no one “right answer” for categorization.

It’s also worth nothing that the technologies and topics surrounding surveillance are rapidly developing and unequally distributed. New stories are sure to emerge, so some of these stories might be become out-of-date. For this reason, this workshop has been explicitly designed as a framework for dissecting any story. If you are aware of a more recent or more topical story when deploying the workshop, we absolutely encourage you to try it out using this format—and let us know how it goes!

No. 1: Buzzfeed Quizzes vs. Democracy (slides 18-19)

While thumbing through your Facebook feed, you see your friend is a French fry, according to a Buzzfeed personality quiz. Curious to find out what type of potato you are, you click on the quiz, click through the page that allows Buzzfeed to access your profile, and take the quiz. You are a tater tot, and you let your friends know. Meanwhile, Buzzfeed remembers your answers, and has also gained access to parts of your private profile (which may include your likes, photos, and friends). They aggregate this data with other users’ information and sell it to Acxiom, a third-party data broker, who turns around and sells it to Clarity Campaigns, a data analytics / political strategy company similar to Cambridge Analytica. Using this data, Clarity Campaigns builds a psychometric model of voters who are likely to be swayed by a well-placed Facebook ad. They help the Bernie Sanders campaign build a digital marketing strategy to target just those types of people using Facebook’s Custom Audience system. Your phone pings, and you see that someone liked your tater tot results. Just beneath that, you see an ad for the Bernie Sanders campaign that is oddly compelling. You keep scrolling.

Source: Vice, The Data That Turned the World Upside Down (Jan. 2017)

No. 2: Very Public Health (slides 21-22)

Cough, cough. Uh oh. You’ve been feeling some symptoms of the infamous coronavirus. Luckily, your city has a rigorous test and trace program. You get tested and…yikes! the result is positive 🙁 Your country’s department of health mandates that you hand over your cell phone so they can view your location history for the last few days, and alert any possible contacts.  A professional contact tracer also asks you for your social media accounts, text messages, credit card statements, and public transportation records. The government promises anything they publish will be anonymous, but they broadcast an alert that includes your age, county, workplace… and the beloved LGBT bar that is a central gathering point for your community. Your neighbor texts you, saying that the alert sounds like you. Luckily, you have already come out to her…but if she could deduce your identity, who else could? Your ex? Your parents?

Source: Time, How South Korea’s Nightclub Outbreak Is Shining an Unwelcome Spotlight on the LGBTQ Community (May 2020)

No. 3: A Dragnet the Size of the Internet (slides 25-26)

You attend a Black Lives Matter protest, coordinating on a group chat with your Google Pixel to your friends so you don’t go alone. A manhunt ensues after the protests because a warrant is issued for protestors who broke windows in the downtown retail stores. A detective with the city police department files a “Dragnet” warrant to Google for location data associated with the stores. Google combs through its Sensorvault database, and sends anonymized location traces that match the time and place. Your location trace is an especially good match, and Google reveals your name. Detectives view your Facebook account and see you are a regular organizer in your community. Your name is added to a list of suspects for inciting a riot.

Source: The New York Times, Tracking Phones, Google Is a Dragnet for the Police (April 2019)

No. 4: Target Knows You’re Pregnant (slides 29-30)

You get pregnant. It’s understandably a scary and exciting time. You still live with your Puritan parents, and they would NOT be happy if they found out. As a pregnant person begins to do, you start searching for and ordering the usual things: unscented lotion, mineral supplements, and cotton balls. Because you still live with your parents, you are cost-conscious, buying each item on the site where it is cheapest. You buy the mineral supplements at Target because they are having a sale, and because you can do in-store pickup to keep things hidden. But buying something at Target means you are now part of their customer database, and your purchases are associated with your “Guest ID.” And buying something at Target using your credit card means your address is now on file. Target uses powerful predictive algorithms to look at your purchases to predict possible future purchases. In an attempt to capture your future purchases, they send a “Congratulations on the baby!” postcard, as well as a coupon for “Goodnight Moon,” to the address on file, which is your parent’s address. D’oh!

Source: Slate, What’s Even Creepier Than Target Guessing That You’re Pregnant? (June 2014)

No. 5: Studded Black Leather Surveillance (slides 33-34)

In your small town, the mall is still a great place to hang out. You are walking with your squad when a pair of studded and shiny boots catch your eye in the window of the Doc Martens store. You pause for a moment. You’ve been looking for a pair of new boots… and a small plastic puck hidden in the door mantle notices, pings your device, “sees” that you paused, and collects your MAC address…. but your friend grabs your arm and hurries you along before you can walk in. Luckily (for them), Doc Martens already has your MAC address on file, because you clicked on an Instagram ad once. Doc Martens’ predictive algorithms know to send even more ads to people, like you, who have paused outside their stores. This time, they advertise their latests boots – the ones that made you pause. You recognize them in the ad, and buy them. Doc Martens’ advertising teams see that the beacon helped make that sale, and begins installing them at every touchpoint they can get their hands on.

Source: The New York Times, In Stores, Secret Surveillance Tracks Your Every Move (June 2019)

No. 6: Just @ Me Next Time (slides 37-38)

You’re finally getting around to writing your thesis. You email your very patient advisor, asking their opinion about a paper you are considering citing. You had no clue, but the author of the paper, now living in Tunisia, is a whistleblower taking refuge from your government, and they are on the NSA’s target list. Your email, travelling on a Sprint network, passes through an AT&T peering center—which is tapped by an NSA router. The router automatically flags your email because it contains a “selector”—the name of the author, and copies it, along with the rest of your inbox, to the NSA server codenamed Pinecone and onto their Mainway and Marina systems. An NSA agent conducting an investigation on the author uses the XKEYSCORE algorithm to search them, and your name comes up. That, in combination with less-than-complimentary tweets about the federal government you wrote in your younger and more radical days, tips the scales, and a predictive algorithm puts you on a no-fly list. You are not alerted until you attempt to fly to present your thesis, and are stopped at the gate. There is no appeal or recourse for your status on the no-fly list.

Sources: The Guardian, No-fly list uses ‘predictive assessments’ instead of hard evidence, US admits (Aug. 2015) & The Hill, 8 surprising times our intel community spied on US citizens (Feb. 2019)

Appendix II: Experience Reports

CtrlZ.AI in Barcelona

• 

• 

• 

Overall, this was a very successful workshop. Here are some of the factors that I think contributed the most that that:

1. ~20 engaged participants: Most of the participants were from the adjacent FAT* conference, and had lots of interesting subject matter knowledge to share. A willingness to volunteer, share insights, and provide feedback made this feel especially productive.
2. Setup: Except for a space to pin up the large map, the distribution of the space and materials felt very seamless. We had four tables, with five chairs at each. At each seat was a handout that included a map on one side and the stories on the other. On each table was a wide variety of colored markers. At the front was a large screen for sharing slides.

There, of course, were some things that could be improved on in the next iteration:

1. Time: We only had 45 minutes allotted for this workshop, and certain aspects of the workshop certainly felt rushed. I believe it is important to get through the entirety of the demo mapping so that participants have an understanding of the process, but it did eat up more than half of our time. By trying to fit it into less than hour, we lost key time for discussion.
   
2. Space to pin up: The (admittedly beautiful) brick walls of the space meant pinning up the large map was tricky. I ended up taping the top portion of the map onto the TV screen. This made it hard for groups to share out their mapping process with the broader group, and made the final large map feel a little “shaky” and underwhelming.

Online with the Tech Equity Coalition

With the pandemic raging, this was the coveillance collective’s first try at leading a digital workshop. Personally, I think it went very well, and was a great learning experience. Some of the factors that contributed to this are:

1. Co-facilitation: We recruited one of our collaborators from the Washington chapter of the Council on American-Islamic Relations to co-lead this workshop. This was especially useful in the Context + Map Overview section. We went back and forth, making it feel more conversational and less like a lecture. We also had awesome members of our collective and the ACLU of Washington to facilitate our break-out rooms, which made for a fluid experience.
   
2. Flexible timing: We had an hour blocked out for this workshop, but with this being a digital workshop I predicted (correctly) that certain aspects would run over. We timed the sections so that all necessary modules would fit into the hour (including having brief dialogues in the breakout groups), and invited participants to stay past the hour for a longer discussion and feedback session. This gave us flexibility and breathing room and allowed our participants to choose their level of involvement based on their schedule.

Of course, there is always things that could be improved on. Some of them include:

1. The Act of (Digital) Mapping: No matter how you slice it, Zoom or Google Slides is simply not as intuitive as drawing with a marker. We found that the digital tools were fiddly and time-consuming, and that participants gained a lot more from the categorizing section of the workshop than the mapping. However, the map was still a useful structure and visual element for understanding the categories, so I don’t recommend dropping the map entirely unless you are pressed for time.

Note: This guide is a work in progress and may change at any time! We’ve done our best to cite our sources, but this page has not been professionally fact-checked.

This workshop was first facilitated for the Tech Equity Coalition in partnership with the ACLU of Washington in August 2020.

What do the recent Black Lives Matter protests have to do with the COVID-19 pandemic? They’ve both triggered a rising tide of surveillance—the use of “smart” technology to track people.

It’s easy to get lost in the news about the latest alarming developments. So we invite you to join the coveillance group for a guided workshop on the deeper principles of how surveillance technology works.

We’ll discuss two key technologies you might have passed by in downtown Seattle without even knowing it: automated license plate readers and fusion centers. Through a series of stories, we’ll show what happens “behind the scenes” to your personal information. Through group discussion, we’ll show how to tell apart what’s new and what’s not new about the way surveillance works today.

This workshop is a pilot session that will run for an hour, so please come prepared to give feedback. Thank you!

Overview and timing

Total: 65 mins

• Buffer (in this time, ask participants to name what they hope to learn from the workshop) — 5 min
• Intro, history, smart streetlights, TOC — 10 min
• ALPRs (brief overview) — 7 min
• Fusion center (brief overview) — 7 min
• Break out into two groups; one for ALPR and one for fusion center — 15 min
  • Monologue — 5 min
  • Discussion and exercise — 10 min
• Share out / closing session + request for feedback — 15 min
  • ALPR group — 2 min
  • Fusion center group — 2 min
  • Quick takeaways — 5 min
  • Big group discussion on interventions — 5 min
• Feedback — 5 min

Slides

Facilitator script

Please read the transcript below in tandem with the slides. Green italics denote a new section, [brackets] denote a new slide.

References

ALPR sources (accessed August 2020)

• NYT ICE surveillance article, mentions plate databases 
  • How ICE Picks Its Targets in the Surveillance Age
• Seattle-specific
  • ALPR SSO doc rebuttal + citations on p41 to previous cases of ALPR abuse
    • http://www.seattle.gov/Documents/Departments/Tech/Privacy/License%20Plate%20Readers_Final%20SIR.pdf — For SDOT
    • https://www.seattle.gov/police-manual/title-16—patrol-operations/16170—automatic-license-plate-readers
    • https://www.seattle.gov/Documents/Departments/Tech/Privacy/SPD%20ALPR%20(Patrol)%20-%20Final%20SIR.pdf — For SPD
  • Police cameras busy snapping license plates (Seattle Times)
  • https://www.seattle.gov/police-manual/title-16—patrol-operations/16170—automatic-license-plate-readers (Seattle policy)
• James Bridle’s article on ANPRS (ALPR in UK)
  • https://medium.com/matter/how-britain-exported-next-generation-surveillance-d15b5801b79e
• EFF heatmap of movements in Oakland
  • https://www.eff.org/deeplinks/2015/01/what-we-learned-oakland-raw-alpr-data
• STOP ALPR page
  • https://www.stopspying.org/latest-news/2019/7/5/automated-license-plate-readers-amp-law-enforcement
• Verge article 
  • ICE has struck a deal to track license plates across the US
• Clear/Learn/Vigilant
  • https://corpwatch.org/article/us-government-using-vigilant-solutions-surveillance-cameras-and-database-track-migrants
  • https://www.thomsonreuters.com/en/press-releases/2017/june/thomson-reuters-brings-vigilant-license-plate-recognition-data-to-clear-investigation-platform.html
  • CLEAR, in their own words https://legal.thomsonreuters.com/en/products/clear-investigation-software?CID=TRSite
• Important EFF sources on ALPR datasets’ scale and sharing
  • https://www.eff.org/pages/automated-license-plate-reader-dataset
  • https://www.eff.org/pages/what-we-learned

Fusion center sources

• “Pacifying the Homeland: Intelligence Fusion and Mass Supervision” (Brendan McQuade)
• (Other sources to be added)

Feedback form

Here’s a sample feedback form on Google Forms: link.

If you’d like to make a separate form for your organization, write to us at team at (our domain) and we can share the Google Form with you.

Note: this guide is a work in progress and may change at any time! We’ve done our best to cite our sources, but this page has not been professionally fact-checked.

This workshop was first facilitated at the CtrlZ.AI zine fair in Barcelona in February 2020.

It’s really stressful to have a body in 2020! Practicing traditional yoga, or other self-care strategies, may not offer specific ways to ease the stress of collectively living under the regime of a “stalker state” (as the Stop LAPD Spying Coalition put it). 

In this workshop, we will guide participants through a series of movement exercises that will expose the principles of the modern American surveillance society—but, you know, in a gentle and relaxing manner. We hope to help participants open their third eyes.

All are welcome; no yoga experience required. If you can, please bring a pillow or blanket.

Script written by Jennifer Lee and coveillance members.

Facilitator information

Goals

• Create a powerful and empowering series of exercises that point to deeper manifestations and resistances to mass surveillance (and more generally, how order is created, kept, maintained, and destabilized)
• Encourage people to question existing assumptions about the ways society functions—the way we move throughout society; the ways we see and are seen
• Energize participants, physically and mentally
• Ideally this workshop is facilitated by two people, though it can work with one as well.

General principles

• Give notice of transitions between poses
• Notice when body touches body
• Move with the breath (inhale and exhale)

Exercises (45 min)

• The exercises (from “facing the faceless” through “collective voice”) total 45 mins.
• The debrief extends 15 minutes after the session.
• A note on the overall flow of energy: there are three ups/downs, which follow a “do and undo” structure. For example, the first ask is to “say your name and line up,” and the undoing of that is to “please choose your name and location.”

Facing the faceless (0 min)

Leader: Facilitator 1

[This is an optional installation that involves custom software that we wrote to erase faces; if you want to try it, email us at team@coveillance.org, otherwise feel free to skip if you don’t want to set up software.]

[There will be a webcam set up with a screen behind it. The software (clmtrackr.js) will erase the faces of everyone who approaches, replacing it with the background image, to create an effect of invisibility.]

Sign at entrance: Welcome to countersurveillance yoga. We invite you to start by experiencing this installation. How does the possibility of visibility and invisibility make you feel? Once you finish interacting with the installation, please enter the room and sit or stand wherever you feel comfortable.

Alternative sign: please leave your faces at the door.

Opening discussion (5 min)

Leader: Facilitator 2

(Once everyone is inside)

Welcome to countersurveillance yoga. My name is Facilitator 2, and this is Facilitator 1. In this workshop, we seek to provide a brief respite from the unique challenges of living in a surveillance society by practicing actions of collective care and resistance in our physical bodies. 

In this workshop, we will move through 7 different flows. In each flow, we will ask you to move your bodies in different ways, but you are always welcome to opt out and do whatever makes you feel most comfortable. Each flow will involve adopting different poses that point to deeper principles of how surveillance societies function. Then, we will embody poses that work to dismantle these types of surveillance thinking.

We’re going to start by taking a minute to start by stretching out our bodies. Feel free to follow along with us, or stretch out in any way that feels good to you. 

Ok, let’s begin!

[1 minute of stretching]

Names (5 min)

Leader: Facilitator 1

Let’s line up in a row, stand up straight, and say our names. 

Then, let’s slowly take that apart…

Let’s start with names. The language used to refer to us. Forget the name you gave us for a second.

We invite you to think about the way you refer to yourself. Hold those names in your head for a second.

We invite you to think of names that your friends use to refer to you.

We invite you also to think of ways that others refer to you. Perhaps authority figures, like teachers, or even the DMV.

And think about how each name makes you feel.

The single static name is an invention meant to make individuals legible to the nation-state. Trackable, measurable–e.g. for receiving services. Institutions understand individuals as a bag of unique identifiers. Name, social security number, phone number, etc.

If you have not already, open yourself to the possibility of going by many names.

If it is available to you, pick a name that you have never used for yourself but that you would like to use, and hold that in your head for the duration of our time together.

Moving and seeing (5 min)

Leader: Facilitator 2

Now that we have explored the feeling of being named and identified, we’re going to exit this row formation, and explore the idea of movement.

This second flow is called moving & seeing. We invite you to move around the room, to exercise freedom of movement without judgment. Please take a moment to find a spot in the room and sit or stand in a position you find comfortable. 

[Facilitator 2 will sit down cross-legged here; Facilitator 1 will be standing or lying down]

Now, we invite you to think about the space and position that you chose. 

For example, some people may want to be more or less visible, and we may see that in the way we’ve chosen to arrange ourselves.

Without needing to say anything to each other, we’ve introduced ourselves. With this exercise, we each have shared our own ways of moving and arranging ourselves, relative to one other.

Seeing seeing (5 min)

Leader: Facilitator 1

Please come out of your pose, and to a standing position. Stretch out a part of your body that feels tense.

With your eyes open, we invite you to look at the wall. Imagine connecting the dots of where you’re looking.

Imagine that your eyes are laser beams that can cut through walls. Expand to moving your head and body to trace the laser beams on the walls around the room. (Focus on the energy of your own line of sight.)

Stop. 

Now, move your head and body to look around the room again, but this time, feel the lines of everyone else’s eyes and gazes moving. Move in whatever direction you feel comfortable. Really see each other. 

Understand that our gazes together have power.

Now, we invite everyone to lie on their backs or sit in a chair. 

Close your eyes. 

Switch off with either leg as necessary if your feet get tired.

Wiggle your toes. Then your ankles, bringing your lower body in.

Pretend that your toes are little cameras encircling the room.

What do you see?

Feeling Information (part I) (5 min)

Leader: Facilitator 2

Now that you’re comfortable in your chosen spot, please find a position that your body finds comfortable. You can either sit on the ground or on a chair, or lie down, whichever you prefer.  We invite everyone to close their eyes for the duration of this exercise.

This next flow is called Feeling Information, Part 1. We are constantly surrounded by flows of information from the moment we wake up to when we go to bed. We are increasingly always reachable, always traceable, and always watchable. We can get so accustomed to the constant flows of information that we consume and share that we may ignore the impacts of such flows on our bodies. 

With this exercise, we want to explore those impacts. 

Take a second to listen to your body. What is it telling you? What information is flowing through it? Is your body tense? Open? Tired? Curious? Anxious? 

Take a moment to tune into the sounds around you. 

Now close your eyes and take a few slow, deep breaths. Take a few moments to loosen your body from your head to your toes. 

Drop your shoulders. Relax the muscles in your arms so that they are heavy like sandbags. 

Relax your facial muscles. 

Release the tension from your jaw and feel your mouth and tongue relax. 

Untighten the muscles around your eyes. 

Breathe in. 1, 2, 3. Breathe out. 1, 2, 3. (Repeat) 

Pick one sensation — such as the feeling of your breath going in and out — and devote your attention to it. Just focus on that. 

Notice the information that your mind keeps returning to and try to understand them like they are clouds in the sky. Notice them, acknowledge them, but let them float on. 

Now with a clear mind, let’s move on. Please hold this pose.

Feeling information (part II) (5 min)

Leader: Facilitator 1

Please keep your eyes closed. 

Our next flow is called Feeling Information, Part 2.

In old times, people used to believe that to take a photograph of someone was to steal their soul.

Now imagine being photographed, and represented as an image. How does that feel in your body? While keeping your eyes closed, can you make a gesture or motion that indicates how that feels?

Please hold the pose, and release the pose.

Please open your eyes and look forward. Please come to a circle and look to the center of the room. Make sure your ears are visible by tucking away your hair. Adopt a neutral expression. This is the pose we’re usually asked to adopt when posing for an ID photo. How does this make you feel? 

Please hold the pose, and release the pose.

Our bodies are being imaged in many different new dimensions today. For example, every time we fill out a form, we trade information about ourselves to access services, like social media or government aid.

Now imagine filling out a form, being imaged in a different way. How does that feel in your body? Can you make a motion or a pose that indicates how that feels? 

Please hold the pose, and release the pose.

Now that we’ve felt the way it feels to be represented as information–we’re going to move into an open phase. Can you bring your body into poses that feel the opposite of how you felt when you were represented in this way? Perhaps you can make light eye contact with each other as it feels right. We invite you to move through a succession of poses.

[Participants start – pause for 20 seconds]

[Facilitator 2 & Facilitator 1 should do goofy poses to get others to loosen up] 

These poses will look and feel different for everybody, but you can do this in any way that feels real to you. Some of you may move into poses demanding that your full, holistic self be seen, others may make yourselves more invisible. 

(1 min) Please come to a neutral, standing position.

Embrace human illegibility (5 min)

Leader: Facilitator 1

Remember your experience going through airport security, or passing through borders. Think of poses you’ve had to adopt—for example, the “hands up, feet apart” pose required by the American TSA. The purpose of this pose is to make your body maximally legible to humans and machines. 

If it is available to you, please adopt this post, and hold it.

Notice the symmetry in this pose. Symmetry is deeply related to conventional notions of “beauty”—maximal symmetry is coded as easy to read, control, and track in its homogeneity. The most average face is the most conventionally beautiful face.

The length of the pose is the amount of time it takes for the xray to finish scanning you. Imagine being inspected by machines, or an airport security agent. What is the quality of that gaze?

Now let’s shake that out! On the other end, we invite you to take on attitudes of ugliness—poses, facial expressions, and voices that may be coded as ugly, to embrace illegibility to humans and machines.

As Mia Mingus writes, “[Consider] the magnificence of a body that shakes, takes up space, spills out, needs help, moseys, slinks… [consider] the magnificence of a body that has been coded, not just undesirable, but inhuman. See [the ugly] for what it is: our greatest strength.”

If it’s available to you, gently rest your gaze on each other, not staring at any one person, and feel your body respond. What is the quality of your gaze, of our looks?

Facilitator 2 will lead us through our final flow.

Collective voice (5 min)

Leader: Facilitator 2

This is a group of people that might not see each other again, but we were united by our time together.

We would like to lead us in a collective voicing, to simulate a coming storm. This represents the ways that we can use our voices and bodies to create collective power. We have the power to make our voices as loud or as quiet as we wish to make them. Please stand or take a seat in a chair and follow along. 

• Rubbing palms together
• Snapping fingers
• Drumming thighs (crescendo)
• Pounding feet (crescendo)
• Drumming thighs (softer) 
• Snapping fingers
• Rubbing palms together

Thank you for joining us in practicing counter-surveillance yoga.

Debrief (15 min)

Leader: Facilitator 2

We would like to invite those who want to share the feelings and thoughts we experienced doing these exercises to remain for a few extra minutes. 

Let’s sit in a circle. Please find a spot and a position in a chair or on the floor so that we’re facing each other. 

We want everyone to have space to talk about how their bodies and minds felt during the workshop. We’d like to ask folks a couple of specific questions, then we’ll open up the conversation to be more freeform.

• Let’s do a quick feelings check-in: Can you describe how you feel right now in one sentence?
• Now that we all had a chance to check in with each other: How did your body feel throughout the workshop?
• What did you find the most surprising experiences during this session?
• Do you have any questions or suggestions for the facilitators?

 If you left your face at the door, please remember to pick it up on the way out.

References

• “Pleasure Activism” by adrienne maree brown
• “Moving Toward the Ugly” by Mia Mingus
• “What Constant Surveillance Does to Your Brain” 
• “Seeing Like a State”
• Detroit Community Technology Project + Our Data Bodies

Overview

• Show the exercise graphic (for participants)
• Show the census and Clearview articles (for participants)
• Show the timeline graphic (for participants)
• Talk about facilitator slides
• Talk about facilitator handout
• Talk about answer key for news articles (facilitator first, then participants)

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Exercise handout and news articles (for participants)

Jan. 9, 2020, NBC News — “Many Latinos believe a citizenship question will be asked on the 2020 census and are less likely to participate, a national Latino leader told Congress on Thursday[…] Last June, the Supreme Court ruled the administration could not add the question about citizenship to the census. The court found the administration’s justification for adding the question was “contrived”. […]“The 2020 census could leave communities across the country undercounted, underrepresented and underfunded,” Maloney said.”

January 21, 2020, CNET.com– “What if a stranger could snap your picture on the sidewalk, then use an app to quickly discover your name, address and other details? A startup called Clearview AI has made that possible, and its app is being used by hundreds of law enforcement agencies in the US, including the FBI, according to a Saturday report in The New York Times. […] The app, says the Times, works by comparing a photo to a database of more than 3 billion pictures that Clearview says it’s scraped off Facebook, Venmo, Youtube and other sites. It then serves up matches, along with links to the sites where those database photos originally appeared. A name might easily be unearthed, and from there other info could be dug up online.The size of the Clearview database dwarfs others in use by law enforcement. The FBI’s own database, which taps passport and driver’s license photos, is one of the largest, with over 641 million images of US citizens.”

Printable version here

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Timeline graphic (for participants)

Printable version here

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Slides (for facilitator)

Current slides here (slides 1-10)

TODO

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Exercise handout (for facilitator)

Exercise: This is new / This is not new

In what new skin will the old snake come forth?
—Frederick Douglass (Black abolitionist and reformer)

History is not the past. It is the stories we tell about the past. How we tell these stories—triumphantly or self-critically, metaphysically or dialectically—has a lot to do with whether we cut short or advance our evolution as human beings.

—Grace Lee Boggs (Chinese-American activist and revolutionary)

Overview

Imagine you see an alarming headline about surveillance in the New York Times or on CNN. How do you tell apart what’s really new about the news from what’s just old news? 

This exercise helps participants work together to recognize the “new skin” of old surveillance thinking narratives (as Frederick Douglass put it) that reinforce the age-old unbalanced power dynamics between different groups of people.

There are no distinct events that mark the exact “moment of being watched,” so we will refer to a number of snapshots in the American history of slavery, racism, indigenous erasure and xenophobia that play into the history of surveillance, criminalization, and disinvestment. 

Goals

1. Equip participants with the mental tools and background to break down new surveillance stories by understanding their historical context
2. Have a narrative understanding of how power dynamics and values of oppression in surveillance thinking 
3. Identify the impacts of surveillance and points of intervention

Supplies

• Printouts of news events
• Printout of timeline
• Instructions to the participants

Instructions

1. Participants split into two groups.
2. Each group is given a news article, along with a framework for understanding what is not new (a timeline of surveillance events) and what is new about our circumstances today (e.g. information technology).
3. Each group breaks down the news article identifying resonances with historical events, aspects that are unprecedented, and intervention points.
4. The two groups reconvene and discuss their findings, with an eye toward analyzing news articles in the wild.

Discussion Questions

This is Not New:

1. Can you find a narratives that link the past events (right) to present events (below)?
2. Where do you see the cycle of resistance, circumvention, and abolition surface? Feel free to add to the timeline and current events

This is New:

3. On your linked narratives, what has changed in method—especially with data availability and data sharing?
4. What interventions can you identify?

Themes and Language to Hold

Communities that have experienced long-held injustices and barriers to resources have been loudly calling attention to issues of access, criminalization, and the logics of oppression beyond surveillance for a while. We defer to those who have been fighting in this realm and highlight the language used in their counter-narrative work.

“Power Not Paranoia”
(A framework for collective strength against the surveillance state)
Our Data Bodies, Stop LAPD Spying Coalition 

“Detroiters Want to Be Seen, Not Watched”
(A special surveillance issue inspired by Our Data Bodies against Detroit surveillance)
Tawana “Honeycomb” Petty, Detroit Community Technology Project

Suggested current events to link to timeline

1. 2020 Census Citizenship Question: Latinos, Asian Americans still fear 2020 census over citizenship question, witnesses tell Congress (NBC News, Jan 2020)
2. Clearview app lets strangers find your name, info with snap of a photo, report says (CNET.com, Jan 2020)

(Old) Motives: What narratives are we fighting?

Racism: Slavery, Lantern Law, Plantation Management, Jim Crow, Redlining, Welfare Queen, War on Drugs, Broken Windows, racial profiling: Stop and Frisk

“Threats to Capital”: Red Scare: Dismantling of unions, surveillance of workers, Welfare Queen myth, criminalization of poverty: Broken Windows, War on Drugs

Xenophobia: Anti-immigration border enforcement, racial profiling (AZ SB †1070), Japanese Internment / Yellow Peril, War on Terror: NYPD Surveilance of Muslim Communities, War on Drugs, ICE workplace raids

“State Enemies”: Japanese Internment, Red Scare (anti-labour), Japanese Internment / Yellow Peril, War on Terror: NYPD Surveilance of Muslim Communities

(New) Methods: What are the big data implications of surveillance?

Volume: Volume is what enables modern day facial recognition and autonomous vehicles, and the store and linkage of large databases provides new modes of identification and tracking of individuals, whether for an ad or a police investigation.

Velocity: The speed at which data gives access to actionable and identifiable information is at a different scale that in the past.

Variety: New private and public data sources, especially in the form of social media, digital trails, and mobile phone data are increasingly tied to our identities.

Veracity: Use of data gives an appearance of objectivity, even if the processing is flawed or the data itself is biased.

Detailed timeline (for facilitator)

Timeline events

• 1712: New York City Slave Revolt: With New York’s dense population, enslaved people were able to occupy public space together. A group of twenty-three slaves incited a revolt to inspire further rebellion, killing nine slave owners and injuring six. 
  • The New York Slave Revolt of 1712 Was a Bloody Prelude to Decades of Hardship (Smithsonian Magazine)
• 1713: The Lantern Law: As a part of crackdown in the city, the Common Council of the City of New York passed “A Law for Regulating Negro & Indian Slaves in the Night Time,” requiring all slaves older than 14 to illuminate themselves with a lantern after sunset, deputizing private citizens to bring in violators to the goals for punishment
  •  Dark Matters: On the Surveillance of Blackness by Simone Browne 
• 1831: Tice Davids Escape: 1st documented escape from slavery via the “Underground Railroad,” a network of abolitionists who aided slaves escape passages to the North.
  • Underground Railroad – HISTORY
• 1850: Fugitive Slave Act: Passed as a part of the compromise of 1850, freed slaves became marked for re-capture, and the act mandated the federal government’s role in to re-capture enslaved people. This was passed to appease southern slave owners. 
  • Original Text: Fugitive Slave Act
• 1890: The US Census puts out a call for new, faster methods to conduct the next census. Hollerith automates the US Census with punch cards, inspired by train ticket conductors that punched fare cards to track the physical characteristics of riders as a part of fare enforcement. This machine is a predecessor to the same census technologies that became the predecessor for IBM
  • Hollerith Census Machine – A Milestone In Big Data Big 
• 1933: German Census
  • Hollerith’s successor, IBM helps the Nazi party take a national census targeted at Jews 
  • Hollerith Machine
• 1942: Japanese Internment 
  • US Census & Commerce create amendment to WWII war powers bill to remove confidentiality protections to track & intern Japanese-ancestry population
  • Confirmed: The U.S. Census Bureau Gave Up Names of Japanese-Americans in WW II
• 1969: Red Squads, COINTELPRO
  • As a part of widespread domestic surveillance of social and political movements by the NYPD in the form of “Red Squads,” the NYPD indict “Panther 21” Black Panther Party on over a hundred of unfounded conspiracy charges
  • The NYPD worked in concert with the FBI’s COINTELPRO operations, which were projects that ran 1956–1971 to infiltrate social movements
  • The trial revealed the extent of pervasive infiltration by police and FBI and sparked a subsequent lawsuit in 1971
  •  Joy James; Duke University (2007). Warfare in the American Homeland: Policing and Prison in a Penal Democracy. books.google.com. p. 74. ISBN 978-0822339236.
• 1971 Handschu Case
  • After the “Panther 21” are acquitted, their lawyers sue & end the practice of social and political surveillance normalized by the NYPD Red Squads. The “Handschu Agreement” establishes oversight limiting domestic spying by NYPD
  • Matt Apuzzo, Adam Goldberg; Simon & Schuster (2013). Enemies Within: Inside the NYPD’s Secret Spying Unit and bin Laden’s Final Plot Against America. books.google.com. p. 44. ISBN 978-1476727936.
• 1985: San Francisco passes the “City of Refuge” resolution
  • San Francisco joins the Sanctuary Movement, a response to Central American refugees refused asylum by the US following US-instigated civil war
  • Debate: How ‘sanctuary cities’ in the US stand up to Federal immigration enforcement
• 2001: Congress passed the PATRIOT Act
  • Following 9/11, Congress gives broad Federal access to civilian phone & business records from private companies
  • Everyone’s heard of the Patriot Act. Here’s what it actually does.
• 2008: Secure Communities Act
  • The joint systems were first asked for under the USA Patriot Act of 2001 and the Enhanced Border Security and Visa Entry Reform Act of 2002 
  • Under the Secure Communities Act, local & state corrections agencies gain access to a joint database linking the FBI’s Integrated Automated Fingerprint Identification System (IAFIS), which contains 66 million criminal records, and the Department of Homeland Security’s (DHS) Automated Biometric Identification System (IDENT), which holds over 90 million immigration records.
  • Secure Communities was created in 2008 when the technology for the joint database was feasible. ICE received 0 from Congress to prioritize and deport foreign nationals who had been “convicted of a crime, sentenced to imprisonment, and who may be deportable.” 
  • Secure Communities justified current data sharing and access that continued into the Trump administration’s deportation policies that increase in the number of raids, the length of detention time, and the number of immigrant detainees without criminal records.
  • Unanswered Questions Surround ICE’s Secure Communities Program 
• 2013: NYPD Spying of Muslims
  • The Handschu case is re-opened in a successful suit against NYPD’s surveillance of communities (the same suit won against the Red Squads)
  • Suit filed on a decade of unjustified surveillance of Muslim communities, including video surveillance, license plate recognition, community mapping, and infilitration
  • Settled with an update to Handschu Agreement that establishes greater oversight on NYPD spying
  • After Spying on Muslims, New York Police Agree to Greater Oversight 
  • Judge Rejects Settlement Over Surveillance of Muslims by New York Police Department 

Sample answer keys (for facilitator and participants)

Example deconstruction: 2020 census

2020 Census Citizenship Question: Latinos, Asian Americans still fear 2020 census over citizenship question, witnesses tell Congress

What’s happening?

The US federal government (particularly President Donald Trump) wanted to ask a citizenship question on the 2020 census: “Is this person a citizen of the United States?” Latino and Asian American communities fear that this creates a hostile environment toward immigrants. This could lead to under-representation and under-resourcing of those communities. The 2020 census is kicking off this month (Jan 2020).

What’s not new about it?

Census data collection has always been a tricky question because of the tension between different ways a community may be seen. The Census has been the basis of American representation and taxation since the 1787 Constitutional Convention that created the United States Constitution and with it, the “three-fifths compromise” of counting slaves as sub-human. 

While you want your community to be counted for representation or resources, you don’t want a “knock on the door” for, say, targeted persecution.

This event points to two broad historical trends: the desire of federal governments to collect information about certain populations to track and control their movements, and the increasing danger of information as it becomes shared in contexts outside of where it was originally collected. If the Census Bureau collects some information, then another arm of the government can deploy this information (e.g. by querying “who are all the people living in Seattle who are not US citizens?”) in a targeted way, e.g. by deporting people who satisfy certain characteristics. Even if not used, the existence of this information can create a “chilling effect” toward the free movement and existence of people of these populations. 

One dangerous precedent was the actions of the Census Bureau in the 1940’s for World War II. As Scientific American writes: 

Despite decades of denials, government records confirm that the U.S. Census Bureau provided the U.S. Secret Service with names and addresses of Japanese-Americans during World War II.

The Census Bureau surveys the population every decade with detailed questionnaires but is barred by law from revealing data that could be linked to specific individuals. The Second War Powers Act of 1942 temporarily repealed that protection to assist in the roundup of Japanese-Americans for imprisonment in internment camps in California and six other states during the war. 

In general, we can see this “old snake” of targeted oppression surface in other past census events as early as a century ago. These include the 1890 automated census with Hollerith machines and 1933 German census. 

Moreover, even as the means of measurement grow more sophisticated, the motivations stay the same: American wartime exceptionalism combined with a xenophobic fear of the “yellow peril” led to certain populations being targeted.

Finally, this event can relate more broadly to the means of monitoring/control of certain populations, such as plantation owners surveilling enslaved peoples by requiring detailed timecards of labor performed. 

What’s new about it?

With old censuses, you had to dig data out of punchcards in a drawer and mail things back and forth; it would take days to make requests through a paper-shuffling bureaucracy. Now, sharing data is almost instantaneous, as easy as a click of a button, with the ability to make powerful queries and filters and discover powerful correlations.

There’s a common big data hype about higher volume, velocity, and variety. This all holds true for information collected by the 2020 census than in past censuses. However, veracity is not really a factor in this story—in fact, the 2020 citizenship census question is the only one that was not “scientifically validated” through the same process as the rest of the census questions.

What can we do to intervene (both past and present)?

Most recently, on the legal level, the Supreme Court blocked this question from being added on the 2020 census. In general, strong data protection policies (particularly policies around data-sharing) would be effective, both legally (such as the GDPR in Europe, and CCPA in California) and technically (building systems that prohibit data-sharing or monitor accesses to data).

Socially, it is important to counter the rhetoric of wartime exceptionalism (“we can weaken protections for individual citizens because the state needs more power, because we’re at war”) by understanding that such protections are difficult to reinstate once eroded, as in the example of the War Powers Act.

Finally, anyone involved in the chain of data-sharing can offer individual acts of resistance: we won’t build it; we won’t share it. For example, in WWII, various sympathetic officers would refuse to turn over addresses of Jews or other targeted populations when asked by the state. 

Example deconstruction: Clearview facial recognition

What’s happening?

• A new startup (Clearview AI) has built a very powerful facial recognition app that threatens to “end your ability to walk down the street anonymously.” 
• News came out about it just this month (Jan 2020).
• It works by combining facial recognition tech with a database of images larger than any entity (including both the US government and big tech companies) has offered before, created by scaping websites. 
• Their tech has already been deployed by federal and tech law enforcement officers to solve cases ranging from shoplifting to murder. It is not yet available to the public, but (according to NYT) Clearview’s investors predict that it will be available eventually to the public.

What’s new about it?

• Uniform blanket deployment (i.e. in all of public space, not just for special circumstances like police pursuing a suspect).
  • This threatens a much larger group of people than ever before, since the tech can be deployed indiscriminately and pervasively, hence the massive news coverage (NYT expose).
• Erosion of social taboos.
  • NYT: Until now, technology that readily identifies everyone based on his or her face has been taboo because of its radical erosion of privacy. Tech companies capable of releasing such a tool have refrained from doing so; in 2011, Google’s chairman at the time said it was the one technology the company had held back because it could be used “in a very bad way.” 
• General lack of regulation of these technologies.
  • They’ve been deployed faster than legislation and public understanding can catch up.
  • …As opposed to, say, guns, which pose a well-known danger in America that the public and lawmakers understand (though are still ill-regulated).
• North American big-city norms of privacy.
  • People actually expect to be able to walk down the street anonymously—which is good! But this is not, say, the norm of privacy in a small town.
• Democratization of information and of information technology.
  • Availability of data — it used to be extremely hard to gather face photos and process them. Now it’s as easy as “surfing the web.”
    • Wired: Ten faces may now seem like a pretty pipsqueak goal, but in 1963 it was breathtakingly ambitious.
    • Wired: In 1973 a Japanese computer scientist named Takeo Kanade made a major leap in facial-recognition technology. Using what was then a very rare commodity—a database of 850 digitized photographs … Kanade developed a program that could extract facial features such as the nose, mouth, and eyes without human input. 
    • NYT: Police departments have had access to facial recognition tools for almost 20 years, but they have historically been limited to searching government-provided images, such as mug shots and driver’s license photos.
  • Advent of personal computing — anyone with an app + internet access can do it. (e.g. One rogue programmer named Hoan Ton-That.)
    •  In recent years, facial recognition algorithms have improved in accuracy, and companies like Amazon offer products that can create a facial recognition program for any database of images.
  • Scale of digital infrastructure — massive compute power is available to developers for cheap; CCTV cameras are everywhere in North American cities.
  • Advances in machine learning in the last 10 years.
    • Wired: Only in the past 10 years or so has facial recognition started to become capable of dealing with real-world imperfection, says Anil K. Jain, a computer scientist at Michigan State University and coeditor of Handbook of Face Recognition. 

What’s not new about it?

• Capitalism (particularly the North American breed). Hurting impacted communities doesn’t (directly) hurt a company’s bottom line!
  • Capitalism drives the understanding of personal data as a resource to mine, and hoarding personal data as a competitive advantage for companies and governments alike.
  • Capitalism gives companies incentives to build and sell technologies to governments and institutions, regardless of impact on communities.
• Social biases in who is building, deploying, and funding new technologies (racism, sexism, pipeline problems, etc.).
  • Wired: Many of the biases that we may write off as being relics of Woody’s time—the sample sets skewed almost entirely toward white men; the seemingly blithe trust in government authority; the temptation to use facial recognition to discriminate between races—continue to dog the technology today.
• Photography/imaging/measuring technologies, especially those involving unique identifiers, are deployed by the state to control vulnerable populations.
  • Examples from the timeline: Bertillonage; Lantern laws; mugshots of prisoners, fingerprints; Polaroid photos to reinforce apartheid.
  • Wired: Over the following year, Woody came to believe that the most promising path to automated facial recognition was one that reduced a face to a set of relationships between its major landmarks: eyes, ears, nose, eyebrows, lips. The system that he imagined was similar to one that Alphonse Bertillon, the French criminologist who invented the modern mug shot, had pioneered in 1879. Bertillon described people on the basis of 11 physical measurements, including the length of the left foot and the length from the elbow to the end of the middle finger. The idea was that, if you took enough measurements, every person was unique. Although the system was labor-intensive, it worked: In 1897, years before fingerprinting became widespread, French gendarmes used it to identify the serial killer Joseph Vacher.
• Poor decision-making frameworks on the part of the tech people who are building it and the law enforcement people who are using it.
  • NYT: Asked about the implications of bringing such a power into the world, Mr. Ton-That seemed taken aback. “I have to think about that,” he said. “Our belief is that this is the best use of the technology.”

What can we do to intervene (both past and present)?

• Work toward abolition.
  • Academics have called facial recognition “the plutonium of AI.”
  • Oakland and San Francisco have passed bans on facial recognition technology.
  • Short of that, pass a strong federal privacy law requiring oversight + procurement processes for surveillance tech.
• Build safeguards into tech from the start to protect vulnerable communities.
  • A poorly-designed Internet, with no safeguards against crawling or mechanism for data control/provenance, enables information to be freely transferred.
• Perform individual acts of resistance.
  • Engineers can say “We won’t build it” and pressure other engineers to do the same; people inside law enforcement agencies can refuse to use these technologies.
• Support pedagogies, sociocultural norms, and capitalistic motives that make it hard for people like the Clearview founder to advance in life.
  • Conventional computer science education will help you answer the question “how do I crawl all the photos on the open web as fast as possible?” but is largely silent on important matters like “but why shouldn’t I do that?”
  • One instructive example is the Dow company, which colluded with the US government to produce napalm for the Vietnam War in 1965. Activists worked to ensure Dow’s reputation reflected this “unethical war profiteering,” and the million napalm contract likely cost Dow billions of dollars.
  • Canadian-trained structural engineers swear by a code of ethics, symbolized by the Iron Ring. Doctors swear by the Hippocratic Oath. Why don’t information workers and software engineers do the equivalent?

References for Clearview

• The Secret History of Facial Recognition (Wired, Jan 2020)
• The Secretive Company That Might End Privacy As We Know It (NYT, Jan 2020)
• Facial Recognition is the Plutonium of AI (Dr. Luke Stark, ACM, April 2019)
• Why Napalm Is a Cautionary Tale for Tech Giants Pursuing Military Contracts (NYT, March 2019)

Other news articles to deconstruct

• How ICE Picks Its Targets in the Surveillance Age (NYT 2019)
• Amazon Ring Doorbells begin partnering with Police Departments (Observer 2019)                          
• China Uses DNA to Map Faces, With Help From the West (NYT, 2019)
• Twelve Million Phones, One Dataset, Zero Privacy (NYT, 2019)
• Iran Tensions Increase Social Media Surveillance at the US Border (Wired 2019)
• What’s this police watchtower doing at a Seattle Safeway? (KUOW 2019)
• Watching the Protesters: These spies may have known too much. (Seattle Weekly, 2010)
• Legal Scholars to LexisNexis, Thompson Reuters: Stop Helping ICE
• 2017 The FBI’s New U.S. Terrorist Threat: ‘Black Identity Extremists’
• Chicago Cops Use Social Media to Track Grieving Families of Gunshot Victims (Medium 2019)
• US, UK, and other governments asks tech companies to build backdoors into encrypted devices (The Verge 2018)
• Chinese facial ID tech to land in Africa
• Palestinian Havard student deported after border search (The Crimson 2019)
• As Asylum Seekers Swap Prison Beds For Ankle Bracelets, Same Firm Profits (NPR 2015)
• MTA fare evasion -> broken windows policing
• How we fought our landlord’s secretive plan for facial recognition—and won (FC 2019)
• We Teach A.I. Systems Everything, Including Our Biases (NYT 2019)
• Sidewalk Labs’ quiet plan for Canada’s banks to manage a national digital ID for health care and housing (boingboing 2019)
• How facial recognition is being used to target sex workers (New Statesman 2019)
• How Facebook Outs Sex Workers (Gizmodo 2017)
• Sex Workers Are at the Forefront of the Fight Against Mass Surveillance and Big Tech (Observer 2019)
• Facial recognition tools shared by ‘Massive, secretive network of police departments’ (boingboing 2019)
• Automation is coming for truckers. But first, they’re being watched. (vox 2017)