8 papers
Drawing Out Legal Risks: Co-Designing with Lawyers to Predict and Manage Legal Uncertainties of Medical AI Tools
Gennie Mansi, Julia Kim, Michael Rosenbloom +1
While there's optimism around medical AI tools due to their abilities to adapt from user-to-user and across environments, these new abilities complicate how people and organization…
Evaluating Actionability in Explainable AI
Gennie Mansi, Julia Kim, Mark Riedl
A core assumption of Explainable AI (XAI) is that explanations are useful to users -- that is, users will do something with the explanations. Prior work, however, does not clearly…
Explainable Reinforcement Learning Agents Using World Models
Madhuri Singh, Amal Alabdulkarim, Gennie Mansi +1
Explainable AI (XAI) systems have been proposed to help people understand how AI systems produce outputs and behaviors. Explainable Reinforcement Learning (XRL) has an added comple…
AI Agents and the Law
Mark O. Riedl, Deven R. Desai
As AI becomes more "agentic," it faces technical and socio-legal issues it must address if it is to fulfill its promise of increased economic productivity and efficiency. This pape…
Understanding the Impact of Physicians' Legal Considerations on XAI Systems
Gennie Mansi, Mark Riedl
Physicians are--and feel--ethically, professionally, and legally responsible for patient outcomes, buffering patients from harmful AI determinations from medical AI systems. Many h…
Implications of Current Litigation on the Design of AI Systems for Healthcare Delivery
Gennie Mansi, Mark Riedl
Many calls for explainable AI (XAI) systems in medicine are tied to a desire for AI accountability--accounting for, mitigating, and ultimately preventing harms from AI systems. Bec…