Responsible AI-What, Why, How and Future!
summary
Starts from the concrete: recounts the Microsoft Tay failure as an explicit cautionary incident, cites tools actually used like IBM Watson Studio and AI monitoring for debiasing hiring, describes State Farm’s Dynamic Vehicle Assessment model predicting total loss and H M’s nine‑point checklist for production reviews. Claims that Microsoft and Google publish governance templates and a “Know Your Data” style tool, argues companies implement checklists and monitoring to make models fairer, more private, and auditable rather than relying on vague principles.
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chapters (13)
- 0:00Responsible AI intro
- 1:55Why responsible AI
- 3:41AI model decisions
- 5:25Key principles
- 7:49Fairness in AI
- 9:15Privacy and security
- 11:00Reliability and safety
- 12:47Transparency in AI
- 13:50Accountability in AI
- 15:15Implementing practices
- 17:45Real-world examples
- 21:18Tech giants' frameworks
- 23:51Future of responsible AI
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