2017/10/18 by Niels Bantilan, Bantilan, Niels · 3 citations
Computer Science · #Computers and Society (cs.CY) #FOS: Computer and information sciences #cs.CY
paper · pdf · doi:10.48550/arxiv.1710.06921
Presented at the Data For Good Exchange 2017
arxiv created 2017/10/18 · arxiv updated 2017/10/20
As more industries integrate machine learning into socially sensitive decision processes like hiring, loan-approval, and parole-granting, we are at risk of perpetuating historical and contemporary socioeconomic disparities. This is a critical problem because on the one hand, organizations who use but do not understand the discriminatory potential of such systems will facilitate the widening of social disparities under the assumption that algorithms are categorically objective. On the other hand, the responsible use of machine learning can help us measure, understand, and mitigate the implicit historical biases in socially sensitive data by expressing implicit decision-making mental models in terms of explicit statistical models. In this paper we specify, implement, and evaluate a "fairness-aware" machine learning interface called themis-ml, which is intended for use by individual data scientists and engineers, academic research teams, or larger product teams who use machine learning in production systems.