Video and data surveillance by public and private entities.
Civil Surveillance (40)
Find narratives by ethical themes or by technologies.
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- 10 min
- New York Times
- 2019
Racial bias in facial recognition software used for Government Civil Surveillance in Detroit. Racially biased technology. Diminishes agency of minority groups and enhances latent human bias.
- New York Times
- 2019
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- 10 min
- New York Times
- 2019
As Cameras Track Detroit’s Residents, a Debate Ensues Over Racial Bias
Racial bias in facial recognition software used for Government Civil Surveillance in Detroit. Racially biased technology. Diminishes agency of minority groups and enhances latent human bias.
Discussion Prompts:
What are the consequences of employing biased technologies to survey citizens? Who loses agency, and who gains agency?
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- 3 min
- techviral
- 2018
In India, where disappearance of children is a common social issue, facial recognition technology has been useful in identifying and located many missing or displaced children. This breakthrough means that the technology can hopefully be applied to help ameliorate this issue, as well as in other areas such as law enforcement.
- techviral
- 2018
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- 3 min
- techviral
- 2018
New Facial Recognition System Helps Trace 3000 Missing Children In Just 4 Days
In India, where disappearance of children is a common social issue, facial recognition technology has been useful in identifying and located many missing or displaced children. This breakthrough means that the technology can hopefully be applied to help ameliorate this issue, as well as in other areas such as law enforcement.
Discussion Prompts:
In what ways does this specific technology serve the common good in India? What are the concerns about the privacy of the children involved, and is this outweighed by the value of safety? To what degree does facial recognition technology actually help solve this problem in general?
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- 5 min
- Gizmodo
- 2020
This article describes the new Amazon Sidewalk feature and subsequently explains why users should not buy into this service. Essentially, this feature uses the internet of things created by Amazon devices such as the Echo or Ring camera to create a secondary network connecting nearby homes which also contain these devices, which is sustained by each home “donating” a small amount of broadband. It is explained that this is a dangerous concept because this smaller network may be susceptible to hackers, putting a large number of users at risk.
- Gizmodo
- 2020
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- 5 min
- Gizmodo
- 2020
You Need to Opt Out of Amazon Sidewalk
This article describes the new Amazon Sidewalk feature and subsequently explains why users should not buy into this service. Essentially, this feature uses the internet of things created by Amazon devices such as the Echo or Ring camera to create a secondary network connecting nearby homes which also contain these devices, which is sustained by each home “donating” a small amount of broadband. It is explained that this is a dangerous concept because this smaller network may be susceptible to hackers, putting a large number of users at risk.
Discussion Prompts:
Why are “secondary networks” like the one described here a bad idea in terms of both surveillance and data privacy? Is it possible for the world to be too networked? How can tech developers make sure the general public has a healthy skepticism toward new devices? Or is it ultimately Amazon’s job to think about the ethical implications of this secondary network before introducing it for profits?
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- 12 min
- Wired
- 2018
This video offers a basic introduction to the use of machine learning in predictive policing, and how this disproportionately affects low income communities and communities of color.
- Wired
- 2018
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- 12 min
- Wired
- 2018
How Cops Are Using Algorithms to Predict Crimes
This video offers a basic introduction to the use of machine learning in predictive policing, and how this disproportionately affects low income communities and communities of color.
Discussion Prompts:
Should algorithms ever be used in a context where human bias is already rampant, such as in police departments? Why is it that the use of digital technologies to accomplish tasks in this age makes a process seem more “efficient” or “objective”? What are the problems with police using algorithms of which they do not fully understand the inner workings? Is the use of predictive policing algorithms ever justifiable?
How Cops Are Using Algorithms to Predict Crimes: How Cops Are Using Algorithms to Predict Crimes-
- 7 min
- MIT Tech Review
- 2020
This article examines several case studies from the year of 2020 to discuss the widespread usage, and potential for limitation, of facial recognition technology. The author argues that its potential for training and identification using social media platforms in conjunction with its use by law enforcement is dangerous for minority groups and protestors alike.
- MIT Tech Review
- 2020
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- 7 min
- MIT Tech Review
- 2020
Why 2020 was a pivotal, contradictory year for facial recognition
This article examines several case studies from the year of 2020 to discuss the widespread usage, and potential for limitation, of facial recognition technology. The author argues that its potential for training and identification using social media platforms in conjunction with its use by law enforcement is dangerous for minority groups and protestors alike.
Discussion Prompts:
Should there be a national moratorium on facial recognition technology? How can it be ensured that smaller companies like Clearview AI are more carefully watched and regulated? Do we consent to having or faces identified any time we post something to social media?
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- 7 min
- The Verge
- 2020
PULSE is an algorithm which can supposedly determine what a face looks like from a pixelated image. The problem: more often than not, the algorithm will return a white face, even when the person from the pixelated photograph is a person of color. The algorithm works through creating a synthetic face which matches with the pixel pattern, rather than actually clearing up the image. It is these synthetic faces that demonstrate a clear bias toward white people, demonstrating how institutional racism makes its way thoroughly into technological design. Thus, diversity in data sets will not full help until broader solutions combatting bias are enacted.
- The Verge
- 2020
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- 7 min
- The Verge
- 2020
What a machine learning tool that turns Obama white can (and can’t) tell us about AI bias
PULSE is an algorithm which can supposedly determine what a face looks like from a pixelated image. The problem: more often than not, the algorithm will return a white face, even when the person from the pixelated photograph is a person of color. The algorithm works through creating a synthetic face which matches with the pixel pattern, rather than actually clearing up the image. It is these synthetic faces that demonstrate a clear bias toward white people, demonstrating how institutional racism makes its way thoroughly into technological design. Thus, diversity in data sets will not full help until broader solutions combatting bias are enacted.
Discussion Prompts:
What potential harms could you see from the misapplication of the PULSE algorithm? What sorts of bias-mitigating solutions besides more diverse data sets could you envision? Based on this case study, what sorts of real-world applications should facial recognition technology be trusted with?