Improving the Fairness and Reliability of AI Solutions
summary
A one-stop Azure ML Responsible AI dashboard ties Fairlearn DICE InterpreML EconML and open-source utilities into an end-to-end workflow that replaces many ad hoc Python scripts. It surfaces error-concentrated heatmaps, cohort comparisons, global and individual feature importances and generates counterfactual suggestions (ten alternatives per case) so teams can see which features flip decisions and catch bizarre rules like gender-based fixes. The demo works live, supports data exploration, mitigation steps and privacy tools such as SmartNoise for deployment.
AI-written summary · about
chapters (15)
- 0:00Start
- 3:46Societal expectations are increasing
- 7:44Responsible Al principles
- 14:18What does this mean to you?
- 15:48Traditional Model Debugging - calculations
- 17:34What's in the Black box?
- 17:58Gaps in model tuning performances
- 22:58Azure Responsible Al Dashboard
- 24:51Error Analysis
- 26:22Model Overview
- 27:36Data Explorer
- 28:12Feature Importance
- 28:56Counterfactuals
- 31:15Manage ML lifecycles responsibly
- 32:47Useful Resources
