Improving Fairness And Equity in Policy Applications of Machine Learning
We are in the early stages of using AI, ML, and Data Science to help make better policy decisions. Governments and nonprofits have started to explore how to improve society by tackling problems such as preventing children from getting lead poisoning, reducing police violence and misconduct, and increasing vaccination rates.
As the impact of the COVID-19 pandemic has continued to increase, both ML researchers and practitioners have been proposing and developing methods to better understand, predict, and mitigate the spread, and governments have been exploring the use of these tools to better guide policy decisions. The AI and ML tools will hopefully be important elements of society’s efforts to overcome COVID-19, but as with any application of machine learning and AI, they also pose risks of introducing or exacerbating disparities. In this talk, I’ll give examples of recent work that highlights the use of ML/AI to achieve fair and equitable outcomes and challenges that need to be tackled in order to have social and policy impact in a fair and equitable manner.
Join Distinguished Career Professor Rayid Ghani in this fascinating discussion. Professor Ghani works with governments and non-profits on policy in health, criminal justice, education, public safety, economic development, and urban infrastructure. He is passionate about teaching practical data science and started the Data Science for Social Good Fellowship that trains computer scientists, statisticians, and social scientists to work on data science problems with social impact. Before joining Carnegie Mellon University, Prof. Ghani was the Founding Director of the Center for Data Science & Public Policy, a Research Associate Professor in Computer Science, a Senior Fellow at the Harris School of Public Policy at the University of Chicago, and Chief Scientist of the Obama 2012 Election Campaign.
DATE: October 1, 2020
TIME: 4:00 PM ET