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Invited talk
in
Workshop: Privacy in Machine Learning (PriML)

Fair Decision Making using Privacy-Protected Data

Ashwin Machanavajjhala

[ ]
2019 Invited talk

Abstract:

Data collected about individuals is regularly used to make decisions that impact those same individuals. We consider settings where sensitive personal data is used to decide who will receive resources or benefits. While it is well known that there is a tradeoff between protecting privacy and the accuracy of decisions, in this talk, I will describe our recent work on a first-of-its-kind empirical study into the impact of formally private mechanisms (based on differential privacy) on fair and equitable decision-making.

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