Non-Discriminatory Machine Learning Through Convex Fairness Criteria


  • Naman Goel EPFL, Lausanne
  • Mohammad Yaghini EPFL, Lausanne
  • Boi Faltings EPFL, Lausanne



machine learning, non-discrimination, fairness


Biased decision making by machine learning systems is increasingly recognized as an important issue. Recently, techniques have been proposed to learn non-discriminatory clas- sifiers by enforcing constraints in the training phase. Such constraints are either non-convex in nature (posing computational difficulties) or don’t have a clear probabilistic interpretation. Moreover, the techniques offer little understanding of the more subjective notion of fairness. In this paper, we introduce a novel technique to achieve non-discrimination without sacrificing convexity and probabilistic interpretation. Our experimental analysis demonstrates the success of the method on popular real datasets including ProPublica’s COMPAS dataset. We also propose a new notion of fairness for machine learning and show that our technique satisfies this subjective fairness criterion.




How to Cite

Goel, N., Yaghini, M., & Faltings, B. (2018). Non-Discriminatory Machine Learning Through Convex Fairness Criteria. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1).