When companies limit the collection of sensitive data, such as race or ethnicity, in order to adhere to good privacy practices, they often find themselves unable to properly test for bias without that same data. This can leave data scientists and lawyers at a loss when it comes to effectively auditing AI models. It means that there is a conflict between the need to train and test AI systems for bias, which requires sensitive data, and the privacy principle of data minimisation, which prefers to avoid collecting such data whenever possible. In this article for the IAPP, Luminos.Law's Brenda Leong, CIPP/US, and Andrew Burt discuss the dilemma and what companies can do about it.
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