Doing Good with Data Science: Fairly and Equitably
Can AI, ML and Data Science help help prevent children from getting lead poisoning? Can it help reduce police violence and misconduct? Can it increase vaccination rates? Can it help cities better prioritize 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 social impact, and more importantly equitable social impact, is not straightforward. I’ll discuss lessons learned from working on 50+ 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. I’ll 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.