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The Unintended Effects of Privacy in Decision and Learning Talks
DescriptionDifferential Privacy has become the go-to approach for protecting sensitive information in data releases and learning tasks that are used for critical decision processes. For example, census data is used to allocate funds and distribute benefits, while several corporations use machine learning systems for financial predictions, hiring decisions, and more. While differential privacy provides strong guarantees, we will show that it may also induce biases and fairness issues in downstream decision processes. In this talk, we delve into the intersection of privacy, fairness, and decision processes, with a focus on understanding and addressing these fairness issues. We first provide an overview of Differential Privacy and its applications in data release and learning tasks. Next, we examine the societal impacts of privacy through a fairness lens and present a framework to illustrate what aspects of the private algorithms and/or data may be responsible for exacerbating unfairness. Finally, we propose a path to partially mitigate the observed fairness issues and discus challenges that require further exploration.