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.

What examples does the speaker provide to illustrate the impact of bias in facial recognition technology?

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How does the speaker propose to improve the understanding and handling of bias in machine learning models?

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What is the significance of the speaker's assertion that bias originates from data?

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