Non-Discriminatory Machine Learning Through Convex Fairness Criteria
Year: 2018
Type: conference-paper
Abstract: 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.
Authors: Naman Goel, Mohammad Yaghini, Boi Faltings
Institution: École Polytechnique Fédérale de Lausanne
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: diamond
OpenAlex ID: W4298377035