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We propose a model-agnostic approach for mitigating the prediction bias of a black-box decision-maker, and in particular, a human decision-maker.
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Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rogriguez, and Krishna P Gummadi · 2017
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Multiaccuracy: Black-box post-processing for fairness in classification
Michael P Kim, Amirata Ghorbani, and James Zou · 2019
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Bias mitigation post-processing for individual and group fairness
Pranay K Lohia, Karthikeyan Natesan Ramamurthy, Manish Bhide, Diptikalyan Saha, Kush R Varshney, and Ruchir Puri · 2019
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A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2019
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Active fairness in algorithmic decision making
Alejandro Noriega-Campero, Michiel A Bakker, Bernardo Garcia-Bulle, and Alex’Sandy’ Pentland · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
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