“Fairwashing” and the Folly of ML Solutionism with Zachary Lipton - TWIML Talk #285
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
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Examining the Gap Between Fairness Claims and Real-World Applications in Machine Learning
This chapter critically examines the claims of fairness in machine learning, emphasizing the discrepancies between theory and real-world applicability. It highlights the dangers of oversimplified models that fail to capture the complexities of actual data and context, particularly at the intersection of research, industry, and media.
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