1:10

Richard Schreiber (speaker) introduces AI bias with an opening hook comparing biased AI to a rigged game, defines AI briefly, and gives real-world harms (hiring, lending). He attributes bias to biased training data, lack of diversity on AI teams, and historical inequalities. Proposed remedies include collecting diverse data, building diverse teams, running regular bias audits, increasing transparency, enabling continuous learning, and enforcing strong ethical guidelines. The clip ends with a call to action to join the movement and contact the creator or website for more information.

Chapters

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