Machine Learning (83)
Find narratives by ethical themes or by technologies.
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- 7 min
- New York Times
- 2018
This article details the research of Joy Buolamwini on racial bias coded into algorithms, specifically facial recognition programs. When auditing facial recognition software from several large companies such as IBM and Face++, she found that they are far worse at properly identifying darker skinned faces. Overall, this reveals that facial analysis and recognition programs are in need of exterior systems of accountability.
- New York Times
- 2018
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- 7 min
- New York Times
- 2018
Facial Recognition Is Accurate, if You’re a White Guy
This article details the research of Joy Buolamwini on racial bias coded into algorithms, specifically facial recognition programs. When auditing facial recognition software from several large companies such as IBM and Face++, she found that they are far worse at properly identifying darker skinned faces. Overall, this reveals that facial analysis and recognition programs are in need of exterior systems of accountability.
What does exterior accountability for facial recognition software look like, and what should it look like? How and why does racial bias get coded into technology, whether explicitly or implicitly?
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- 7 min
- CNN
- 2021
The South Korean company Supertone has created a machine learning algorithm which has been able to replicate the voice of beloved singer Kim Kwang-seok, thus performing a new single in his voice even after his death. However, certain ethical questions such as who owns artwork created by AI and how to avoid fraud ought to be addressed before such technology is used more widely.
- CNN
- 2021
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- 7 min
- CNN
- 2021
South Korea has used AI to bring a dead superstar’s voice back to the stage, but ethical concerns abound
The South Korean company Supertone has created a machine learning algorithm which has been able to replicate the voice of beloved singer Kim Kwang-seok, thus performing a new single in his voice even after his death. However, certain ethical questions such as who owns artwork created by AI and how to avoid fraud ought to be addressed before such technology is used more widely.
How can synthetic media change the legacy of a certain person? Who do you believe should gain ownership of works created by AI? What factors does this depend upon? How might the music industry be changed by such AI? How could human singers compete with artificial ones if AI concerts became the norm?
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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.
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?
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- 10 min
- Gizmodo
- 2021
Physicist Brian Nord, who learned about deep learning algorithms through his research on the cosmos, warns against how developing algorithms without proper ethical sensibility can lead to these algorithms having more negative impacts than positive ones. Essentially, an “a priori” or proactive approach to instilling AI ethical sensibility, whether through review institutions or ethical education of developers, is needed to guard against privileged populations using algorithms to maintain hegemony.
- Gizmodo
- 2021
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- 10 min
- Gizmodo
- 2021
Developing Algorithms That Might One Day Be Used Against You
Physicist Brian Nord, who learned about deep learning algorithms through his research on the cosmos, warns against how developing algorithms without proper ethical sensibility can lead to these algorithms having more negative impacts than positive ones. Essentially, an “a priori” or proactive approach to instilling AI ethical sensibility, whether through review institutions or ethical education of developers, is needed to guard against privileged populations using algorithms to maintain hegemony.
What would an ideal algorithmic accountability organization or process look like? What specific ethical regions should AI developers study before creating their algorithms? How can algorithms or other programs created for one context, such as scientific research or learning, be misused in other contexts?
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- 6 min
- CBS News
- 2021
In light of the recent allegations of Facebook whistleblower Frances Haugen that the platform irresponsibly breeds division and mental health issues, AI Specialist Karen Hao explains how Facebook’s “algorithm(s)” serve or fail the people who use them. Specifically, the profit motive and a lack of exact and comprehensive knowledge of the algorithm system prevents groundbreaking change from being made.
- CBS News
- 2021
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- 6 min
- CBS News
- 2021
Facebook algorithm called into question after whistleblower testimony calls it dangerous
In light of the recent allegations of Facebook whistleblower Frances Haugen that the platform irresponsibly breeds division and mental health issues, AI Specialist Karen Hao explains how Facebook’s “algorithm(s)” serve or fail the people who use them. Specifically, the profit motive and a lack of exact and comprehensive knowledge of the algorithm system prevents groundbreaking change from being made.
Do programmers and other technological minds have a responsibility to understand exactly how algorithms work and how they tag data? What are specific consequences to algorithms which use their own criteria to tag items? How do social media networks take advantage of human attention?
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- 5 min
- Time
- 2021
In 2021, former Facebook employee and whistleblower Frances Haugen testified to the fact that Facebook knew how its products harmed teenagers in terms of body image and social comparison; yet because of their interest in their profit model, they do not significantly attempt to ameliorate these harms. This article provides four key lessons to learn from how Facebook’s model is harmful.
- Time
- 2021
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- 5 min
- Time
- 2021
4 Big Takeaways From the Facebook Whistleblower Congressional Hearing
In 2021, former Facebook employee and whistleblower Frances Haugen testified to the fact that Facebook knew how its products harmed teenagers in terms of body image and social comparison; yet because of their interest in their profit model, they do not significantly attempt to ameliorate these harms. This article provides four key lessons to learn from how Facebook’s model is harmful.
How does social quantification result in negative self-conception? How are the environments of social media platforms more harmful in terms of body image or “role models” than in-person environments? What are the dangers of every person having easy access to a broad platform of communication in terms of forming models of perfection? Why do social media algorithms want to feed users increasingly extreme content?