• 
• 6 min
• Wired
• 2019

Spreading of harmful content through Youtube’s AI recommendation engine algorithm. AI helps create filter bubbles and echo chambers. Limited user agency to be exposed to certain content.

• Digital Media Misinformation
• Digital Addiction
• Recommender Systems
• Social Networks

• 
• 5 min
• Business Insider
• 2020

This article tells the story of Timnit Gebru, a Google employee who was fired after Google refused to take her research on machine learning and algorithmic bias into full account. She was terminated hastily after sending an email asking Google to meet certain research-based conditions. Gebru is a leading expert in the field of AI and bias.

• Tech Monopolies
• Technology and Race
• Machine Learning

• 
• 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

• 
• 7 min
• Chronicle
• 2021

The history of AI contains a pendulum which swings back and forth between two approaches to artificial intelligence; symbolic AI, which tries to replicate human reasoning, and neural networks/deep learning, which try to replicate the human brain.

• Singularity
• Scientific Innovation
• Machine Learning
• Networks

• 
• 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.

• Civil Surveillance
• Algorithmic Bias
• Machine Learning
• Biometrics

• 
• 5 min
• Venture Beat
• 2021

Relates the story of Google’s inspection of Margaret Mitchell’s account in the wake of Timnit Gebru’s firing from Google’s AI ethics division. With authorities in AI ethics clearly under fire, the Alphabet Worker’s Union aims to ensure that workers who can ensure ethical perspectives of AI development and deployment.

• Tech Monopolies
• Technology and Race
• Machine Learning