The people, podcasts, and papers to check out on AI bias
AI bias is a rapidly changing field. Below you’ll find the people to follow to stay on top of it, along with the books, papers, podcasts, and other resources you need to get up to speed.


AI bias is a rapidly changing field. Below you’ll find the people to follow to stay on top of it, along with the books, papers, podcasts, and other resources you need to get up to speed.
Innovation for All (particularly this episode and this one)
Exponential View (particularly this episode)
Your Undivided Attention, from the Center for Humane Technology
Meredith Whittaker, AI Now
Dorothea Baur, consultant
Arvind Narayanan, Princeton University
Cathy O’Neil, algorithmic auditor
Safiya Umoja Noble, UCLA
Joanna Bryson, University of Bath
Annette Zimmerman, Princeton University
Kate Crawford, AI Now
Jacob Metcalf, Data & Society
Jason Schultz, NYU, AI Now
John C. Havens, IEEE
Adam Cutler, IBM
Cassie Kozyrkov, Google
Joy Buolamwini, Founder, Algorithmic Justice League, MIT
Maria Axente, PwC UK
Chelsea Barabas, MIT
Michael Kearns, University of Pennsylvania
Regulating Artificial Intelligence Systems: Risks, Challenges, Competencies, and Strategies, Harvard Journal of Law and Technology
The Intuitive Appeal of Explainable Machines, Fordham Law Review
Studying Up: Reorienting the study of algorithmic fairness around issues of power, Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency
From ethics washing to ethics bashing: a view on tech ethics from within moral philosophy, Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency
Owning Ethics: Corporate Logics, Silicon Valley, and the Institutionalization of Ethics, Data & Society
Algorithms to Live By, Brian Christian and Tom Griffiths
Algorithms of Oppression: How search engines reinforce racism, Safia Umoja Noble
Weapons of Math Destruction: How big data increases inequality and threatens democracy, Cathy O’Neil
Biased: Uncovering the hidden prejudice that shapes what we see, think, and do, Jennifer L. Eberhardt

Other resources
Algorithmic Impact Assessments: a practical framework for public agency accountability, AI Now