News Article (136)
Find narratives by ethical themes or by technologies.
FILTERreset filters-
- 7 min
- Venture Beat
- 2021
As machine learning algorithms become more deeply embedded in all levels of society, including governments, it is critical for developers and users alike to consider how these algorithms may shift or concentrate power, specifically as it relates to biased data. Historical and anthropological lenses are helpful in dissecting AI in terms of how they model the world, and what perspectives might be missing from their construction and operation.
- Venture Beat
- 2021
-
- 7 min
- Venture Beat
- 2021
Center for Applied Data Ethics suggests treating AI like a bureaucracy
As machine learning algorithms become more deeply embedded in all levels of society, including governments, it is critical for developers and users alike to consider how these algorithms may shift or concentrate power, specifically as it relates to biased data. Historical and anthropological lenses are helpful in dissecting AI in terms of how they model the world, and what perspectives might be missing from their construction and operation.
Whose job is it to ameliorate the “privilege hazard”, and how should this be done? How should large data sets be analyzed to avoid bias and ensure fairness? How can large data aggregators such as Google be held accountable to new standards of scrutinizing data and introducing humanities perspectives in applications?
-
- 10 min
- The New Yorker
- 2020
This article contextualizes the BLM uprisings of 2020 in a larger trend of using social media and other digital platforms to promote activist causes. A comparison between the benefits of in-person, on-the-ground activism and activism which takes place through social media is considered.
- The New Yorker
- 2020
-
- 10 min
- The New Yorker
- 2020
The Second Act of Social Media Activism
This article contextualizes the BLM uprisings of 2020 in a larger trend of using social media and other digital platforms to promote activist causes. A comparison between the benefits of in-person, on-the-ground activism and activism which takes place through social media is considered.
How should activism in its in-person and online forms be mediated? How does someone become an authority, for information or otherwise, on the internet? What are the benefits and detriments of the decentralization of organization afforded by social media activism?
-
- 10 min
- The Washington Post
- 2021
The academic Philip Agre, a computer scientist by training, wrote several papers warning about the impacts of unfair AI and data barons after spending several years studying the humanities and realizing that these perspectives were missing from the field of computer science and artificial intelligence. These papers were published in the 1990s, long before the data-industrial complex and the normalization of algorithms in the everyday lives of citizens. Although he was an educated whistleblower, his predictions were ultimately ignored, the field of artificial intelligence remaining closed off from outside criticism.
- The Washington Post
- 2021
-
- 10 min
- The Washington Post
- 2021
He predicted the dark side of the Internet 30 years ago. Why did no one listen?
The academic Philip Agre, a computer scientist by training, wrote several papers warning about the impacts of unfair AI and data barons after spending several years studying the humanities and realizing that these perspectives were missing from the field of computer science and artificial intelligence. These papers were published in the 1990s, long before the data-industrial complex and the normalization of algorithms in the everyday lives of citizens. Although he was an educated whistleblower, his predictions were ultimately ignored, the field of artificial intelligence remaining closed off from outside criticism.
Why are humanities perspectives needed in computer science and artificial intelligence fields? What would it take for data barons and/or technology users to listen to the predictions and ethical concerns of whistleblowers?
-
- 7 min
- The New York Times
- 2021
On October 4th, 2021, Facebook’s servers experienced an outage which left its apps, including the commonly used Facebook, Instagram, and Whatsapp, out of commission for several hours. The problem is said to be caused by a incorrect configuring of Facebook’s servers, which ultimately led to a Domain Name System error in which the numerical IP addresses determined by the computer became inaccessible. The myriad effects of this outage spread across the globe as businesses were effected by the lack of access to these social networks. Additionally, certain other internet services linked to Facebook became inaccessible.
- The New York Times
- 2021
-
- 7 min
- The New York Times
- 2021
Facebook and all of its apps go down simultaneously.
On October 4th, 2021, Facebook’s servers experienced an outage which left its apps, including the commonly used Facebook, Instagram, and Whatsapp, out of commission for several hours. The problem is said to be caused by a incorrect configuring of Facebook’s servers, which ultimately led to a Domain Name System error in which the numerical IP addresses determined by the computer became inaccessible. The myriad effects of this outage spread across the globe as businesses were effected by the lack of access to these social networks. Additionally, certain other internet services linked to Facebook became inaccessible.
What are the dangers of relying on fallible networks to perform essential functions such as business? How can network infrastructure be more protected? How much data and information should Facebook be trusted with?
-
- 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
-
- 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.
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?
-
- 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
-
- 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.
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?