Eunsuk Kang
Required reading: Os Keyes, Jevan Hutson, Meredith Durbin. A Mulching Proposal: Analysing and Improving an Algorithmic System for Turning the Elderly into High-Nutrient Slurry. CHI Extended Abstracts, 2019.
Recidivism scenario: Should a person be detained?
Recidivism scenario: Should a defendant be detained?
Fairness-aware Machine Learning, Bennett et al., WSDM Tutorial (2019).
Holstein, Kenneth, Jennifer Wortman Vaughan, Hal Daumé III, Miro Dudik, and Hanna Wallach. "Improving fairness in machine learning systems: What do industry practitioners need?" In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1-16. 2019.
"Fairness and Machine Learning" by Barocas, Hardt, and Narayanan (2019), Chapter 1.
(P[R=1|A=a])/(P[R=1|A=b])≥0.8
For details on other types of fairness metrics, see: https://textbook.coleridgeinitiative.org/chap-bias.html
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries, Olteanu et al., Frontiers in Big Data (2016).
Bias in Online Freelance Marketplaces, Hannak et al., CSCW (2017).
Fairness-aware Machine Learning, Bennett et al., WSDM Tutorial (2019).
Datasheets for Dataset, Gebru et al., (2019). https://arxiv.org/abs/1803.09010
Q. How can we modify an existing dataset or change the data collection process to reduce bias?
"Big Data processes codify the past. They do not invent the future. Doing that requires moral imagination, and that’s something only humans can provide. " -- Cathy O'Neil in Weapons of Math Destruction
O'Neil, Cathy. Weapons of math destruction: How big data increases inequality and threatens democracy. Broadway Books, 2016.
Delayed Impact of Fair Machine Learning. Liu et al., (2018)