Why a World with AI Needs More EQ
summary
Computing power reaches parity with estimated human-brain calculations by midcentury and workforce studies predict automation shifts jobs toward social and creative skills. Data show women face stark losses—for every 20 lost jobs only 1 stem job gained—and roughly 50% more likely injury from male‑biased airbag tests until 2010. Name‑swap and CV experiments with thousands of professors show white male applicants get more responses and higher competence ratings; 886 recommendation letters label men as “star” researchers while calling women “teachers.” Berkeley rolls out an Engineering 1 course and workshops plus a corporate diversity survey to counteract bias.
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chapters (20)
- 0:00Introduction and welcome
- 1:27My connection to AI
- 4:13Academic contributions to AI
- 6:21AI's societal impact
- 7:06Job market risks from AI
- 8:10Gender gap in tech jobs
- 9:35Women in high-tech workforce
- 10:38High attrition in tech careers
- 12:04Bias in AI design examples
- 13:48Voice recognition bias
- 15:12Why diverse teams matter
- 16:17Business case for diversity
- 17:20Root causes of disparity
- 18:04Unconscious bias in hiring
- 19:07CV bias study results
- 20:33Letters of recommendation bias
- 21:37Berkeley's diversity initiatives
- 23:01Workshops for equity and inclusion
- 24:27Corporate diversity survey
- 24:47Closing and call to action
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