Ethics, Machine Learning and Racial Equity

Organized by Structural Racism SIG - System Dynamics Society

The ethical considerations of using data in modeling work on structural racism and racial equity require us to go beyond the standard considerations that inform ethical conduct of research and practice. Because such work has immediate implications for perpetuating or disrupting racial injustice, and many societies including the US have racial bias embedded in their operating structures, intensive use of data to inform system dynamics modeling from problem and system conceptualization to model validation carries the potential risk of perpetuating the racial bias in system dynamics modeling of racial equity. While critical assessment of secondary data has been stressed as essential to scientific modeling using system dynamics, the accessibility of Big Data and advances in machine learning raise new questions about when and how machine learning for racial equity can play a role. In this panel, we will hear from leaders working in the field of Big Data, Artificial Intelligence, and Machine Learning about the challenges and best practices of ethically engaging in racial justice work from their lens.

Panelists:

Donald Martin, Sr. Staff Technical Program Manager and Social Impact Technology Strategist at Google

Kristian Lum, Assistant Research Professor, Department of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania