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Survey on Fairness Notions and Related Tensions
Fairness requirements to be satisfied while learning models created several types of tensions among the different notions of fairness and other desirable properties such as privacy and classification accuracy.
Reducing Unintended Bias of ML Models on Tabular and Textual Data
Unintended biases in machine learning (ML) models are among the major concerns that must be addressed to maintain public trust in ML.
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.
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