Can AI, ML and Data Science help prevent children from getting lead poisoning? Can it reduce infant and maternal mortality? Can it reduce police violence and misconduct? Can it help cities better target limited resources to improve lives of citizens and achieve equity? We’re all aware of the potential of ML and AI but turning this potential into tangible and equitable social impact takes cross-disciplinary training, new methods, and dealing with explainability and bias & fairness challenges. In this talk, Rayid Ghani will discuss lessons learned from working on 60+ projects over the past few years with non-profits and governments on high-impact public policy and social challenges in criminal justice, public health, education, economic development, public safety, workforce training, and urban infrastructure. He will highlight opportunities as well as challenges around explainability and bias/fairness that need to tackled in order to have social and policy impact in a fair and equitable manner.
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