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How organizations must rethink data governance for AI: new challenges around training data provenance, model outputs, and the blurred line between data and code.
This summary was generated from show notes and public descriptions, not from a full transcript review. Details may contain inaccuracies.
Canon
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Data governance policies create the environment within which AI teams operate. Strict governance constrains but ensures quality; lax governance enables speed but risks bias and legal exposure.
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Data scientists can control the quality of their data pipelines and analysis but cannot control organizational politics around data access and budget. The Stoic approach focuses on technical excellence.
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