• 
• 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
• Slate
• 2021

A new law passed unanimously in Maine heavily restricts the contexts in which facial recognition technology can be deployed, putting significant guardrails around how it is used by law enforcement. Also, it allows citizens to sue if they believe the technology has been misused. This is a unique step in a time when several levels of government, all the way up to the federal government, are less likely to attach strict rules to the use of facial recognition technology, despite the clear bias that is seen in the wake of its use.

• Civil Surveillance
• Technology and Race
• Computer Vision
• Biometrics

• 
• 5 min
• Kinolab
• 2019

In an imagined future of London, citizens all across the globe are connected to the Feed, a device and network accessed constantly through a brain-computer interface. In this narrative, Ben, a member of the family who owns the company which created the Feed, uses the augmented reality features to create a virtual version of his ex-wife, Miyu, who he can make indulge in his own fantasies, regardless of what those may be. Eventually, this digital version of Miyu starts to glitch, but Ben nonetheless begins to share this virtual, subservient clone to other people to use in their own fantasies.

• Deep Fake
• Gender Bias
• Brain-Computer Interfaces
• Augmented Reality

• 
• 13 min
• Kinolab
• 2016

“Hidden Figures” chronicles the journeys of Katherine Johnson (Taraji P. Henson), Dorothy Vaughan (Octavia Spencer), and Mary Jackson (Janelle Monáe), three black women who worked on the space missions at the Langley Research Center in Hampton, Virginia in 1961. All three women persist against segregation and abject racism as they climb the ladder and make important contributions to the space mission. While Katherine becomes the first black woman on Al Harrison’s Space Task Group, Mary Jackson pursues her dream of becoming an engineer at NASA by petitioning to take courses at an all white school, and Dorothy Vaughan attempts to learn the programming language Fortran in order to ensure that herself and fellow human computers are not replaced by the newest IBM 7090 computer.

• Digital Divides and Inequality
• Gender Bias
• Technology and Race
• Machine Learning

• 
• 7 min
• Vice
• 2019

Programmer creates an application that uses neural networks to remove clothing from the images of women. Deepfake technology being used against women systematically, despite continued narrative that its use in the political realm is the most pressing issue.

• Deep Fake
• Gender Bias
• Machine Learning
• Object Recognition

• 
• 15 min
• Hidden Switch
• 2018

A hands-on learning experience about the algorithms used in dating apps through the perspective of a created monster avatar.

• Limitations of Digital Technologies
• Algorithmic Bias
• Machine Learning
• Social Networks