Title: Themis-ml: A Fairness-aware Machine Learning Interface for End-to-end Discrimination Discovery and Mitigation,
Abstract: 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.