Machine Learning Systems Design with Sara Hooker: Algorithmic bias

The transcript discusses the complexities of algorithmic bias, emphasizing that it is not solely a data problem but also involves how models are designed and trained. It explores the role of model capacity in mitigating bias and highlights the need for evolving definitions of bias that reflect societal changes. The conversation underscores the responsibility of researchers in designing models that minimize harm, as most models are used off-the-shelf in various applications.

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