Teaching Your Models to Play Fair | Global AI Student Conf

It is very important to ensure fairness while building an AI system that can scale to a large number of users. Thus, I plan to first talk about how fairness is important while building AI apps. I would then go on to talk about how FairLearn helps us in doing so specifically with the easy to use dashboard interface. I plan to show how FairLearn could be used to assess model fairness and how it does so. I would then talk about mitigation strategies with FairLearn so as to remove the biases with state of the art models and compare multiple models to perform this successfully. As time persists I would also show live demos about using FairLearn and Azure ML to assess and mitigate fairness in an AI system.

Transcript

1. Teaching Your Models to Play Fair Rishit Dagli @rishit_dagli
7. • Interpretability • Privacy • Security
17. • Property of a person does not affect outcome
18. Motivation
21. Source: Ziyuan Zhong
26. • Dashboards
27. • Dashboards
28. • Dashboards
29. • Dashboards
30. • Dashboards
31. • Dashboards
32. • Dashboards
33. • Dashboards • Mitigation strategies
34. • Dashboards • Mitigation strategies
37. bit.ly/fairness-deck
39. Thank You Rishit Dagli @rishit_dagli