Jeffrey Grogger, University of Chicago
Abstract
Violence against women is tragically common, with an estimated one in three women globally experiencing physical or sexual violence in their lifetime. Reducing re-offending is an important part of reducing overall violence, as a considerable share of those who perpetrate violence go on to re-offend. Risk assessments are used in several OECD countries to identify offending risk, but there are limits to how well they work in practice; their predictive power has been found to be fairly low, and the assessments – informed by professional judgment – can be inconsistently applied. How can AI be used to improve predictions of domestic violence re-offending, and to better prioritise a response for those at highest risk?
Join us as Dr. Jeff Grogger, University of Chicago, presents research on the use of machine learning techniques to prevent domestic violence re-offending, ahead of International Women’s Day. The event will feature a presentation from Jeff, followed by a Q&A with the audience, moderated by Monika Queisser, Head of the Social Policy Division.