Dalila Paquiot

Ensuring ethical and inclusive machine learning during this time is a challenge. In this eye-opening technical talk, we’ll dive into case studies that illustrate the critical need for fairness in machine learning. Through demonstrations, we’ll explore how fairness toolkits can be leveraged to uncover and mitigate societal biases in machine learning models. Attendees will get an insider’s look at the practical techniques used by model practitioners to assess model fairness, diagnose the root causes of unfairness, and implement effective bias mitigation strategies. By the end of the session, the audience will walk away with a toolbox of resources and a new sense of purpose. After this session you should have a starting point for building ethical and accountable models, so don’t miss an opportunity to be a force for positive change."