Building ethical AI: Transparency, bias mitigation, and trust
summary
Details how generative AI’s nondeterminism widens harms and maps bias sources across the ML lifecycle, cites a Bloomberg stable diffusion result that links high‑pay occupations to white men, highlights ImageNet geographic skew and historical/representation/evaluation biases, prescribes group‑wise fairness metrics, synthetic data and Google fairness tools, recommends UX/product guardrails for medical prompts, and operationalizes defenses with CI/CD fairness alerts and explainability tradeoffs against model performance.
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chapters (15)
- 0:00Introduction
- 1:56Generative AI challenges
- 3:43Defining bias
- 5:31ML lifecycle bias sources
- 8:20Historical bias
- 10:50Representation bias
- 11:56Fairness metrics
- 13:44Black box problem
- 15:28Mitigating bias solutions
- 16:53People-first AI
- 19:23Ethical harm scenarios
- 21:08Data quality fairness
- 23:40Explainability user-specific
- 25:30Deploying ethical AI challenges
- 26:11Closing remarks
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