Spring 2019

In the algorithmic fairness reading group we discuss and present current work on algorithmic fairness.

  • Tuesday, 5 Feb 2019, 15:00–16:00. Denise and Josephine present: Friedler et al., A comparative study of fairness-enhancing interventions in machine learning, FAT* ‘19 Proceedings of the Conference on Fairness, Accountability, and Transparency. Pages 329-338 Atlanta, GA, USA — January 29 - 31, 2019. arxiv:1802.04422
  • Tuesday, 12 Feb 2019, 15:00–16:00. Thore present: Cynthia Dwork et al.: Fairness through awareness. Innovations in Theoretical Computer Science 2012: 214-226. arXiv 1104:3913

Suggested Papers for Presentation

  • Sorelle A. Friedler, Carlos Scheidegger, Suresh Venkatasubramanian: On the (im)possibility of fairness. arXiv:1609.07236
  • Jon M. Kleinberg, Sendhil Mullainathan, Manish Raghavan: Inherent Trade-Offs in the Fair Determination of Risk Scores. Innovations in Theoretical Computer Science 2017: 43:1-43:23. arXiv 1609:05807
  • Sampath Kannon, Aaron Roth, Juba Ziani: Downstream Effects of Affirmative Action, FAT* 19, ACM DL
  • Moritz Hardt, Eric Price, Nathan Srebro: Equality of Opportunity in Supervised Learning , NIPS’16. arxiv 1610.02413

Additional readings

  • Solon Barocas, Moritz Hardt, Arvind Narayanan: Fairness and Machine Learning, Draft