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Towards a Systems-Level Approach to Fair ML with Sarah M. Brown - #456

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

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Understanding Simpson's Paradox through Admission Data

This chapter delves into Simpson's paradox, showcasing how aggregated data can distort reality and lead to misleading conclusions. Using UC Berkeley's admission rates as a case study, it demonstrates the importance of analyzing data at a granular level and introduces a tool to help detect discrepancies and biases in data interpretation.

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