Responsible AI
summary
Microsoft presents concrete governance: six principles — fairness, reliability and safety, privacy and security, inclusivity, transparency, accountability — plus a co‑designed fairness checklist with about 48 practitioners. Practical tools include AutoML voting ensemble on a UCI bike rental dataset, InterpretML feature attributions showing working day, temperature and humidity contributions, Fairlearn for bias checks, ML pipelines for CI/CD, homomorphic encryption, differential privacy and confidential computing; demos run and visualizations work, not just abstract guidance.
AI-written summary · about
chapters (11)
- 0:00Introduction
- 1:45Responsible AI
- 3:30Why Responsible AI
- 7:00Opportunities
- 8:40Finance
- 9:40The Debate
- 17:05Tools
- 20:00ML Pipeline
- 26:40Top Key Features
- 34:10Regulatory Bodies
- 36:40Conclusion
related talks
32mOperationalizing Responsible AI
34mImproving the Fairness and Reliability of AI Solutions
31mOperationalizing Responsible AI
40mResponsible AI Practices: Fairness in ML
34mResponsible AI: The Key to Ethical, Safe and Inclusive Software Development
39mSponsored Session: The Search for Transparency and Accountability in the Age of AI: RAI & XAI as Essential Tools
38mResponsible AI In Practice
39mBuilding a Responsible AI Governance Program