• 
• 7 min
• The Verge
• 2019

Reliance on “emotion recognition” algorithms, which use facial analysis to infer feelings. Credibility of the results in question based on inability of machines to recognize abstract nuances.

• Limitations of Digital Technologies
• Computing Ethics Issues
• Computer Vision
• Machine Learning

• 
• 7 min
• ZDNet
• 2020

Dr. Gary Marcus explains that deep machine learning as it currently exists is not maximizing the potential of AI to collect and process knowledge. He essentially argues that these machine “brains” should have more innate knowledge than they do, similar to how animal brains function in processing an environment. Ideally, this sort of baseline knowledge would be used to collect and process information from “Knowledge graphs,” a semantic web of information available on the internet which can sometimes be hard for an AI to process without translation to machine vocabularies such as RDF.

• Limitations of Digital Technologies
• Scientific Innovation
• Machine Learning
• Networks

• 
• 10 min
• MIT Technology Review
• 2020

This article explains the ethical warnings of Timnit Gebru against training Natural Language Processing algorithms on large language models developed on sets of textual data from the internet. Not only does this process have a negative environmental impact, it also still does not allow these machine learning tools to process semantic nuance, especially as it relates to burgeoning social movements or countries with lower internet access. Dr. Gebru’s refusal to retract this paper ultimately lead to her dismissal from Google.

• Technology Based Environmental Impact
• Limitations of Digital Technologies
• Algorithmic Bias
• Machine Learning
• Data Mining

• 
• 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.

• Civil Surveillance
• Limitations of Digital Technologies
• Technology and Race
• Machine Learning
• Biometrics

• 
• 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.

• Limitations of Digital Technologies
• Digital Activism
• Social Networks

• 
• 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.

• Control of Personal Data/Identity
• Limitations of Digital Technologies
• Machine Learning
• Recommender Systems