{"ID":5439476,"CreatedAt":"2026-07-01T01:17:58.482524686Z","UpdatedAt":"2026-07-02T18:49:48.32244458Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.30828","arxiv_id":"2606.30828","title":"Drawing Out Legal Risks: Co-Designing with Lawyers to Predict and Manage Legal Uncertainties of Medical AI Tools","abstract":"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 organizations are able to predict and manage risk based on existing laws and regulations. Lawyers are trained to identify potential legal outcomes, but they lack technical AI knowledge, making it difficult to translate their expertise to creators and users of AI tools. We contribute insights from our co-design process with U.S. lawyers to identify and translate ways to predict and manage risks of medical AI tools. We present the visualizations we developed through two years of cross-disciplinary efforts and thereby illustrate our findings about how legal risks are determined and our strategies for people and organizations to predict and manage these risks. We offer insights about leveraging lawyers' expertise to understand, predict, and manage legal risks.","short_abstract":"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 organizations are able to predict and manage risk based on existing laws and regulations. Lawyers are trained to identify potential legal outcomes, but...","url_abs":"https://arxiv.org/abs/2606.30828","url_pdf":"https://arxiv.org/pdf/2606.30828v1","authors":"[\"Gennie Mansi\",\"Julia Kim\",\"Michael Rosenbloom\",\"Mark Riedl\"]","published":"2026-06-29T18:59:15Z","proceeding":"cs.HC","tasks":"[\"cs.HC\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}