AI and Gender: Building Fair AI Systems for the Future
summary
Concrete numbers and concrete fixes: only 28% aware of gender bias, 66% want government regulation and people give tech companies only 32% trust; documented failures include Apple Card 10× limits on men versus women, LinkedIn CV filtering 2016→fixed 2022, biased Amazon recruitment, gendered voice defaults in Siri/Alexa and facial‑recognition errors. Mitigations are tangible: audit and clean data, apply fairness metrics, run gender impact assessments, diversify teams, add human oversight and transparency, and several companies do rollbacks and fixes that measurably reduce harm.
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chapters (12)
- 0:00Introduction
- 0:28AI gender bias stats
- 2:34Defining gender bias
- 4:40How bias enters AI
- 8:59Ethical challenges in AI
- 10:25Real-world AI bias examples
- 14:18Why bias persists in AI
- 18:19Policy frameworks for fairness
- 22:15Steps to mitigate AI bias
- 24:41Cultural change in AI
- 25:44AI and social power
- 26:05Q&A
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