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 presents: Cynthia Dwork et al.: Fairness through awareness. Innovations in Theoretical Computer Science 2012: 214-226. arXiv 1104:3913
  • Tuesday, 19 Feb 2019, 15:00–16:00. Martin presents: Sorelle A. Friedler, Carlos Scheidegger, Suresh Venkatasubramanian: On the (im)possibility of fairness. arXiv:1609.07236
  • Tuesday, 26 Feb 2019, 15:00-16:00. Matteo presents: L. Elisa Celis, Lingxiao Huang, Vijay Keswani, Nisheeth K. Vishnoi: Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees, FAT* ‘19 Proceedings of the Conference on Fairness, Accountability, and Transparency. Pages 319–328 Atlanta, GA, USA — January 29 - 31, 2019. arXiv:1806.06055
  • Tuesday, 5 Mar 2019, 15:00-16:00. Tobias presents: 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
  • Tuesday, 12 Mar 2019, 15:00-16:00. Frederik presents: Moritz Hardt, Eric Price, Nathan Srebro: Equality of Opportunity in Supervised Learning , NIPS’16. arxiv 1610.02413

Suggested Papers for Presentation

  • Sampath Kannon, Aaron Roth, Juba Ziani: Downstream Effects of Affirmative Action, FAT* 19, ACM DL
  • T. Kamishima, S. Akaho, H. Asoh, and J. Sakuma: Fairness-Aware Classifier with Prejudice Remover Regularizer, ECMLPKDD 2012 ResearchGate
  • S. Corbett-Davies, S. Goel: The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning, arXiv 1898:00023
  • B. Green, L. Hu: The Myth in the Methodology: Towards a Recontextualization of Fairness in Machine Learning, ICML18
  • T. Calders, S. Verwer: Three naive Bayes approaches for discrimination-free classification, Data Min. Knowl. Discov. 21 ResearchGate
  • N. Kilbertus, M. Carulla, G. Parascandolo, M. Hardt, D. Janzing, B. Schölkopf: Avoiding discrimination through causal reasoning, NIPS17

Additional readings

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