The Man, The Machine, And The Black Box: ML Observability

In this talk, Aparna Dhinakaran, Co-Founder and CPO of Arize AI, covered the challenges organizations face in checking for model fairness, such as the lack of access to protected class information to check for bias and diffuse organizational responsibility of ensuring model fairness. Aparna also dived into the approaches organizations can take to start addressing ML fairness head-on with a technical overview of fairness definitions and how practical tools such as ML Observability can help build ML fairness checks into the ML workflow. If you've heard of Michelangelo, Aparna built part of the model store, which was eventually kind of integrated into Michelangelo. After that, Aparna actually went to a Ph.D. program in Computer Vision, at which time she started thinking about things like ML fairness, how does bias get introduced into our models, especially models like facial recognition? At that point, as a researcher, Aparna was realising that she couldn't even answer basic questions about model performance or model service metrics.