FairPut: A Light Framework for Machine Learning Fairness with LightGBM

8 Pages Posted: 29 Jun 2020 Last revised: 22 Oct 2020

See all articles by Derek Snow

Derek Snow

The Alan Turing Institute

Date Written: June 4, 2020

Abstract

This is a holistic framework to approach fair prediction outputs at the individual and group level. This framework includes quantitative monotonic measures, residual explanations, benchmark competition, adversarial attacks, disparate error analysis, model agnostic pre-and post-processing, reasoning codes, counterfactuals, contrastive explanations, and prototypical examples. A number novel techniques are proposed in this framework, each of which could benefit from future examination.

Keywords: Fairness, Explainability, Machine Learning, Robustness, Python, FairPut

JEL Classification: O, C, J

Suggested Citation

Snow, Derek, FairPut: A Light Framework for Machine Learning Fairness with LightGBM (June 4, 2020). Available at SSRN: https://ssrn.com/abstract=3619715 or http://dx.doi.org/10.2139/ssrn.3619715

Derek Snow (Contact Author)

The Alan Turing Institute ( email )

British Library, 96 Euston Rd
London, NW1 2DB
United Kingdom

HOME PAGE: http://www.turing.ac.uk/

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