Why we need more Women in Tech

on YouTube ↗13m

" summary

Cites a survey of 1404 respondents and a dataset audit finding only 169 female voices reflected in training data, arguing that biased datasets and hiring pipelines skew machine learning and product decisions; points to Google Microsoft Facebook Twitter as examples and calls for concrete fixes: audits, quotas, leadership development programs, stronger work policies and company-run networking initiatives. Describes deploying these changes at company level and measuring outcomes against retention and promotion rates.

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" chapters (4)

1. 0:00Start
2. 7:27SOLUTIONS
3. 8:38CHALLENGES
4. 9:22WHAT COMPANIES CAN DO

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