Data Ethics Panel
summary
Panelists give concrete examples and operational guidance: a supermarket’s facial recognition wrongly accuses a customer and research like Gender Shades shows ~35% error rate for darker-skinned women versus ~1% for light-skinned men, consent and access controls are used in production with a consent management platform and role-based access policies, a government algorithm impact assessment is available online, and companies run a quarterly feature review that blocks features if mitigations aren’t sufficient.
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chapters (11)
- 0:00Introduction
- 1:54Defining data ethics
- 4:24Why data ethics matters
- 7:15Balancing harm and business
- 10:29Ethics into actionable steps
- 13:20Data minimization trade-offs
- 16:35LLM training data ethics
- 19:02AI accountability governance
- 23:10Human error vs AI
- 26:41Ethics in education
- 31:39Closing remarks
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