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s-LIME: Reconciling Locality and Fidelity in Linear Explanations
The benefit of locality is one of the major premises of LIME, one of the most prominent methods to explain black-box machine learning models.
Making ML models fairer through explanations: the case of LimeOut
To illustrate, we will revisit the case of "LimeOut" that was proposed to tackle "process fairness", which measures a model's reliance on sensitive or discriminatory features.
LimeOut: An Ensemble Approach To Improve Process Fairness
To achieve both, we draw inspiration from "dropout" techniques in neural based approaches, and propose a framework that relies on "feature drop-out" to tackle process fairness.
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