webinar register page

Webinar banner
Predictive Risk Modeling - Analytics and Child Welfare: An Examination
The NYU McSilver Institute & the Fordham University Graduate School of Social Service invite you to an important learning opportunity that will include panels and an interactive conversation about how predictive risk modeling may be used, and is being used, in child welfare practice. Learn what predictive modeling risk is, how this "tool" is currently being used outside New York, and the concerns advocates and others have raised, including with regard to re-creating disproportionate representation of Black families in the child welfare/family regulation system.

Hear from two experts who have helped design predictive risk models; an official from Los Angeles County where predictive modeling is being implemented; the ACLU, which has conducted a national survey on predictive analytics in child welfare systems; and from an authority on how people react emotionally and internally, often unconsciously, to these and other issues that are connected to race and bias.

Moderator:
Michael A. Lindsey, PhD, MSW, MPH
Executive Director, McSilver Institute
Constance and Martin Silver Professor of Poverty Studies, NYU Silver School of Social Work
Aspen Health Innovators Fellow, The Aspen Institute

Panelists:
J. Khadijah Abdurahman
Fellow at Columbia University, AI Now Institute, UCLA

Jennie Feria, LCSW
Deputy Director, Los Angeles County Department of Children and Family Services

Aaron Horowitz
Chief Data Scientist, ACLU

Emily Putnam-Hornstein, PhD
John A. Tate Distinguished Professor for Children in Need & Director of Policy Practice,
UNC Chapel Hill School of Social Work
Co-Director, Children's Data Network

Anjana Samant
Senior Staff Attorney, Women's Rights Project, ACLU

Rhema Vaithianathan
Professor, Social Data Analytics, The University of Auckland

Nov 10, 2021 05:00 PM in Eastern Time (US and Canada)

Webinar logo
Webinar is over, you cannot register now. If you have any questions, please contact Webinar host: Fordham GSS Events.