When AI Meets RI: Legal and Compliance Considerations Emerging from the Use of AI Tools to Identify Research Integrity Concerns This is the third in a series of posts addressing the use of AI-based screening tools to detect research integrity issues, including pros and cons, unique challenges, downstream implications, potential pitfalls, and risk mitigation practices. Building on our second post, we continue looking at the unique challenges to assessing and addressing research misconduct claims detected through the use of AI-based screening tools: AI-based tools make it relatively easy to lodge numerous complaints in a short period. An influx of research misconduct claims can lead to numerous workstreams and far-reaching considerations, both within the institution and externally, as shown below. Confidentiality requirements attendant to research misconduct review processes can be a complicating factor. Managing these claims and the ensuing reviews can affect, and require input from, many institutional constituencies including: Lab Members & Staff Faculty/PIS Clinical Operations Research Administration RI Apparatus Public Relations Leadership Legal Compliance Human Resources Information Technology Tech Transfer/IP Institutional Review Board Philanthropy