Аннотация
We analyze different notions of fairness in decision making when the
underlying model is not known with certainty. We argue that recent notions of
fairness in machine learning need to be modified to incorporate uncertainties
about model parameters. We introduce the notion of subjective fairness as
a suitable candidate for fair Bayesian decision making rules, relate this
definition with existing ones, and experimentally demonstrate the inherent
accuracy-fairness tradeoff under this definition.
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