Correspondences between Privacy and Nondiscrimination: Why They Should Be Studied Together
This foundational work clarifies theoretical connections between privacy and nondiscrimination, aiding researchers and policymakers in addressing ethical AI challenges.
The paper formalizes the relationship between privacy and nondiscrimination by identifying causal and statistical versions for each, and links their differences to Bayesian vs. frequentist probability interpretations, enabling cross-application of research results.
Privacy and nondiscrimination are related but different. We make this observation precise in two ways. First, we show that both privacy and nondiscrimination have two versions, a causal version and a statical associative version, with each version corresponding to a competing view of the proper goal of privacy or nondiscrimination. Second, for each version, we show that a difference between the privacy edition of the version and the nondiscrimination edition of the version is related to the difference between Bayesian probabilities and frequentist probabilities. In particular, privacy admits both Bayesian and frequentist interpretations whereas nondiscrimination is limited to the frequentist interpretation. We show how the introduced correspondence allows results from one area of research to be used for the other.