At this extraordinary moment in US history, the evils of racism become clear. It's no secret that technology has played a role in promoting and spreading racism. This is an ideal time to read, listen and learn. Below are many resources – research, articles, and books – that deal with the interface between race and bias in technology, especially in the area of AI. This is a starting point for the training that all responsible citizens should acquire.
Gender shades – Milestone work by Joy Buolamwini, Dr. Timnit Gebru, Dr. Helen Raynham and Deborah Raji, who studies how facial recognition systems work in different genders and races.
Language cancellation – A piece with a spoken word, inspired by Research led by Allison Koenecke This shows how five popular speech recognition systems perform worst among African American Vernacular English speakers.
AI Nows Algorithmic Accountability Policy Toolkit – A resource of the AI Now Institute that, according to the organization's website, is aimed at advocates interested in understanding the government's use of algorithmic systems.
The NIST study evaluates the effects of race, age and gender on facial recognition software – A report from the National Institute of Standards and Technology (NIST), part of the U.S. Chamber of Commerce.
StereoSet: A measure of the distortion in language models – MIT work that “measures racism, sexism and otherwise discriminatory behavior in one model while ensuring that the performance of the underlying language model remains strong”.
Discriminatory systems: gender, race and power in AI – Research by the AI Now Institute that examines the extent and extent of the diversity crisis in AI.
The future of work in black America – A McKinsey report examining how automation can widen the wealth gap between black and white families in the United States.
Promotion of racial competence in technology – Work from the Data & Society project from Dr. Jessie Daniels, Mutale Nkonde and Dr. Darakhshan Mir explains why "ethics, diversity of attitudes and implicit bias training are not enough" to establish real racial competence in the technology world.
Machine preload – A Pro Publica article that shows how predictive algorithms in criminal justice are biased against blacks.
Technological elites, meritocracy and postracial myths in Silicon Valley – A book chapter in which Dr. Safiya Noble and Dr. Sarah Roberts examines "how discourses among Silicon Valley's technocratic elites invest in post-racism as a pretext for capital consolidation as opposed to political commitments to end discriminatory labor practices," the executive summary said.
Some important books on race and technology are: Suppression algorithms from Dr. Safiya Noble; Race for technology by Ruha Benjamin; Technicolor: race, technology and everyday life by Alondra Nelson; Race, rhetoric and technology from Dr. Adam J. Banks; and Artificial unintelligence: How computers misunderstand the world by Meredith Broussard.