Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making

Explore a conference talk examining the challenges and design needs for fairness and accountability in algorithmic decision-making within high-stakes public sector contexts. Delve into insights from interviews with 27 machine learning practitioners across five OECD countries, uncovering the disconnect between organizational realities and current research in usable, transparent, and discrimination-aware machine learning. Discover potential design opportunities, including tools for tracking concept drift in secondary data sources and building transparency mechanisms for both managers and frontline public service workers. Gain valuable perspectives on ethical challenges and future directions for collaboration in critical applications such as taxation, justice, and child protection.