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Fairwashing: the risk of rationalization
[article]
2019
arXiv
pre-print
Black-box explanation is the problem of explaining how a machine learning model -- whose internal logic is hidden to the auditor and generally complex -- produces its outcomes. Current approaches for solving this problem include model explanation, outcome explanation as well as model inspection. While these techniques can be beneficial by providing interpretability, they can be used in a negative manner to perform fairwashing, which we define as promoting the false perception that a machine
arXiv:1901.09749v3
fatcat:dpzzjeobdnaubgcrdhstzkp7lq