Themis-ml: A Fairness-Aware Machine Learning Interface for End-To-End Discrimination Discovery and Mitigation

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 bu...

Full description

Saved in:
Bibliographic Details
Published in:Journal of technology in human services 2018-01, Vol.36 (1), p.15-30
Main Author: Bantilan, Niels
Format: Article
Language:English
Subjects:
Algorithms
Application programming interface
Artificial intelligence
Bias
Decision analysis
Decision making
Discovery
Discrimination
discrimination discovery
fairness-aware machine learning
Hiring
Learning
Machine learning
Mental models
Mitigation
Parole
Process engineering
social bias
Statistical models
statistics
Teams
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 article 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.
ISSN:1522-8835
1522-8991
DOI:10.1080/15228835.2017.1416512