Navigating Bias and Fairness in Machine Learning

Today we’re joined by Sandra Wacther, an associate professor and senior research fellow at the University of Oxford. 

Sandra’s work lies at the intersection of law and AI, focused on what she likes to call “algorithmic accountability”. In our conversation, we explore algorithmic accountability in three segments, explainability/transparency, data protection, and bias, fairness and discrimination. We discuss how the thinking around black boxes changes when discussing applying regulation and law, as well as a breakdown of counterfactual explanations and how they’re created. We also explore why factors like the lack of oversight lead to poor self-regulation, and the conditional demographic disparity test that she helped develop to test bias in models, which was recently adopted by Amazon.

The complete show notes for this episode can be found at twimlai.com/go/521.