How gender imbalance is holding back the future of AI
summary
Identifies concrete failures: voice assistants default to female personas, Google Translate flips Serbian pronouns, facial recognition and health AIs lack women’s data. Cites UNDP study and community datasets showing 30% university enrollment, 23% workforce and <10% C‑level representation and a ~9% ICT pay gap in Serbia. Describes interventions—mentorships, journey maps, an R&D lab study and volunteer-run events—and notes some fixes (image models improving) but many gaps persist because datasets and hiring pipelines remain biased.
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chapters (6)
- 0:00Introduction
- 3:19Gender imbalance effects
- 18:37UNP research: women in STEM Serbia
- 27:59Steps to mitigate imbalance
- 37:14Barriers in senior leadership
- 46:08Closing remarks
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