Adaptive Learning Strategies
The discussion highlights the complexities of optimizing for fairness in machine learning, particularly when different contexts have varying definitions of toxicity. Emphasizing the importance of adaptive development sets, the conversation explores how supervised methods can evolve over time while maintaining a single optimization criterion. Additionally, the potential of self-supervised learning is examined, revealing its ability to produce better representations despite the challenge of intelligibility.In this clip
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Machine Learning Street Talk (MLST)
#92 - SARA HOOKER - Fairness, Interpretability, Language Models
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