“Ethics and Equity in Developing Artificial Intelligence models for Patients at Risk of Sepsis” SUMMARY The goal of this K23 supplement application proposal is to create ethical, disease-specific statistical bias detection for prediction models. The proposal introduces the first two steps of such as system: convene a ethics-driven focus group to identify important demographic factors for which to consider correcting, when applicable; and (2) create a novel bias detection metric, “Selection and Information Bias Exposure and Rank” or SIBER, which details the involvement and relative importance of each demographic factor (selected by the focus group) to the prediction output of existing models. In the first part of the workflow, a multidisciplinary group of ethicists, data scientists, clinicians, and community-based healthcare advocates are asked to attend three 2-hour sessions focused on improving health equity in the disease of interest (e.g., sepsis). At the end of the three sessions, the group is expected to have identified the demographic groupings needed for the algorithmic component. The data from the focus groups will be analyzed using qualitative analytic techniques. Among the results will be the list of demographic groups/labels that are at risk for experiencing healthcare bias. A utility function of each demographic variable will be created to weigh their relative importance in prediction output, but only among those who are deemed at high risk of bias. (The process for determining bias is beyond the scope of this proposal, but will be in future work.) The bias detection system (SIBER) will be exposed to two different sepsis prediction models, one of which being the model deliverable for aim 1 of my K23. The models will determine sepsis risk on unseen data. The proposal will test the ability of SIBER to identify and rank the different demographic factors contributing to wide prediction intervals. This supplement builds upon the ongoing work of aim 1 in the parent K23 award, which is to derive and validate SOS using electronic medical record data. Sepsis is the test case to prove the utility of SIBER, but the work proposed in this supplement is critical to creating equitable predictive models in general. It demonstrates that selection and information bias is present in the electronic medical record, and attempts to link it with certain demographic factors. It is the first step in operationalizing healthcare justice using data. It also demonstrates the intersectionality that exists between many demographic factors.