Reading List

Many of these readings from a Data Ethics class at the University of San Francisco, syllabus prepared by Dr Rachel Thomas. Still a great class, though now taught by someone else. Enrolls once or twice a year.

• What is ethics?
• A framework for understanding sources of harm throughout the machine learning process
• Do artifacts have politics?
• An ethical toolkit for engineering / design practice
• Anthropological/Artificial Intelligence & the HAI
• The Incompatible Incentives of Private Sector AI
• How Algorithms Can Learn to Discredit “the Media”: Defamation is efficient, and AIs may have already figured it out
• The Role and Limits of Principles in AI Ethics
• Better, Nicer, Clearer, Fairer: A Critical Assessment of the Movement for Ethical Artificial Intelligence and Machine Learning
• Transcript of Dr. Rumman Chowdhury's talk on Algorithmic Colonialism at IntersectTO 2019
• The Call for Trauma‐Informed Design Research and Practice, Rachael Dietkus, Social Workers Who Design (2022)