Lecture 13: Justice, Fairness, Bias (Part 1) PDF (updated, broken links fixed: 4 March)
Required: Harini Suresh and John Guttag. 2021. A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle. In Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO '21). Association for Computing Machinery, New York, NY, USA, Article 17, 1–9.
Required: Lepri, B., Oliver, N., Letouzé, E. et al. Fair, Transparent, and Accountable Algorithmic Decision-making Processes. Philos. Technol. 31, 611–627 (2018).
Optional Video: The Trouble with Bias - NIPS 2017 Keynote - Kate Crawford
Lecture 14: Paper Discussion: PDF
READ THE PAPER BEFORE ATTENDING THE LECTURE!
Required: Selbst, Andrew D. and Boyd, Danah and Friedler, Sorelle and Venkatasubramanian, Suresh and Vertesi, Janet, Fairness and Abstraction in Sociotechnical Systems (August 23, 2018). 2019 ACM Conference on Fairness, Accountability, and Transparency (FAT*), 59-68