Here’s some reading that I’ve enjoyed or am looking forward to on AI Ethics and Fair ML:

Popular science // non-fiction books

  • Weapons of math destruction — Cathy O’Neil: Nice outline of what I believe is the biggest ethical concern regarding ML systems: They reinforce existing biases and inequalities.

  • Data Feminism — Catherine D’Ignazio & Lauren F. Klein: A tour of data/AI ethics from a feminist standpoint; feminism here meant in its broadest intersectional sense (i.e. they also consider race, ability status, etc.). Quite heavy on the examples. This book is freely available online .

  • Algorithms of Oppression — Safiya Umoja Noble: Focuses on race and representation in the context of search engines. 

  • Race after technology — Ruha Benjamin: I haven’t read this one yet, here’s what Wikipedia says: “Race After Technology: Abolitionist Tools for the New Jim Code is a 2019 American book focusing on a range of ways in which social hierarchies, particularly racism, are embedded in the logical layer of internet-based technologies.”

  • The Alignment Problem: Machine Learning and Human Values — Brian Christian: I haven’t read this one yet, it seems to be a pretty broad intro to the ethics of ML.

  • Invisible Women — Caroline Criado Perez: A lot of our world is designed in a data-driven way, but for historical reasons this data is often data collected on men (for example in the military). As a consequence women are, for example, 47% more likely to get injured in car accidents. Urgh. Can also recommend the related 99% invisible podcast episode.

  • The Age of Surveillance Capitalism — Shoshana Zuboff: An analysis of the problematic economics of big data.

  • Artificial Unintelligence — Meredith Broussard: “A guide to understanding the inner workings and outer limits of technology and why we should never assume that computers always get it right.”

Science writing

  • FairML book — Solon Barocas, Moritz Hardt & Arvind Narayanan: A textbook on fair machine learning giving an overview of the relatively young field. This is for technical readers (nothing scary but contains some math/ml). Available here.

  • FAccT conference proceedings : Latest research trends within Fairness, Accountability and Transparency in ML.

Blogs etc.

  • Algorithmic Justice League. The amazing Joy Buolamwini who started the AJL initiative and her collaborators made a documentary, it’s on Netflix.

  • An article on why I believe it’s useful working on ML Fairness.