Oct. 5 (Tuesday), 12:00: Ben Green (Postdoctoral Scholar in the Michigan Society of Fellows, Asst. Prof. Public Policy, Univ. of Michigan), presents as part of our series, Biased AI
---> Zoom Registration: https://uncc.zoom.us/meeting/register/tJMqdOGtqTMvHtwUJylS_jTV_YvCiCOEThwd
Abstract: Governments increasingly use algorithms (such as machine learning predictions) to distribute resources and make important decisions. Although these algorithms are often hailed for their ability to improve public policy implementation, they also raise significant concerns related to racial oppression, surveillance, inequality, technocracy, and privatization. While some government algorithms demonstrate an ability to advance important public policy goals, others—such as predictive policing, facial recognition, and welfare fraud detection—exacerbate already unjust policies and institutions. The issues with these tools cannot be boiled down to straightforward engineering challenges. This talk will explore some of the epistemic, political, and institutional factors that lead to algorithmic harms and will introduce an agenda for developing and regulating government algorithms in the interest of equity and social justice.
About the series: Artificial Intelligence (AI) systems are poised to offer potentially revolutionary changes to fields as diverse as healthcare and traffic systems. However, there is a growing concern both that deployment of AI systems is increasing social power asymmetries and that ethical attention to those asymmetries requires going beyond technical solutions and incorporating research on unequal social structures. Because AI systems are embedded in social systems, technical solutions to bias need to be contextualized in their interaction with those larger systems. This series explores problems and solutions in making AI more just. Coming speakers include Serena Wang and Alex Hanna, and talks will be archived on the Center's YouTube Channel.