Papers › FairPut: A Light Framework for Machine Learning Fairness with LightGBM
FairPut: A Light Framework for Machine Learning Fairness with LightGBM
Derek Snow
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.
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