How to Fix AI - Solutions to ML Bias - And Why They Don't Matter

Explore solutions to machine learning bias and their real-world implications in this thought-provoking conference talk from Strange Loop. Delve into the complexities of algorithmic fairness as Joyce Xu, an AI/ML engineer from Sidewalk Labs, presents an in-depth, intuitive explanation of deep learning techniques designed to combat underlying data bias. Gain insights into measurable aspects of algorithmic fairness and examine case studies of real-world systems. Challenge conventional thinking about AI bias solutions as Xu argues for algorithms resilient to biased data and questions whether optimizing for fairness alone addresses the root of the problem. Learn about ML concepts, privacy-preserving solutions in urban mobility and sustainability, and the intersection of AI with history and urban studies in this 45-minute presentation that encourages a critical reframing of AI bias issues.

"How to Fix AI: Solutions to ML Bias (And Why They Don't Matter)" by Joyce Xu