Thinking Critically about Machine Learning and its Social Impacts
This presentation will explore approaches to understanding and tackling issues around fairness and bias in machine learning technology. It will also be a chance to discuss some of the unanswered and hard questions, as well as the potential for innovative solutions in this emerging field.
Jamila Smith-Loud is a researcher and policy advocate who is committed to challenging implicit or assumed problem identifications in communities of color and advocates for community-based research to inform practice and social change. She is currently a User Researcher at Google on the Ethical Machine Learning Team, where she uses research to advocate for the needs and perspectives of diverse users. Prior to Google, she was the Manager of Strategic Initiatives at a Los Angeles based civil rights non-profit, Advancement Project, where she supported the development of racial equity initiatives through research, analysis, and advocacy. Jamila also participated in the Political and Legal Anthropology Review Fellowship Program, where her research focused on the intersections between law, power, identity, and cultural change.