CSAI: Week 3

Lecture 5: Justice, Fairness, Bias (Part 1) PDF

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 Video: The Trouble with Bias - NIPS 2017 Keynote - Kate Crawford

Optional: Lepri, B., Oliver, N., Letouzé, E. et al. Fair, Transparent, and Accountable Algorithmic Decision-making Processes. Philos. Technol. 31, 611–627 (2018).

Lecture 6: Justice, Fairness, Bias (Part 2) PDF

Required:  D’Ignazio, C., & Klein, L. (2020). 2. Collect, Analyze, Imagine, Teach. In Data Feminism. Retrieved from https://data-feminism.mitpress.mit.edu/pub/ei7cogfn

Optional Video: 21 Definitions of Fairness - Arvind Narayanan

Optional: Lundgard, Alan, Measuring Justice in Machine Learning (2020) 2020 ACM Conference on Fairness, Accountability, and Transparency (FAT*)