When AI Meets RI: Legal and Compliance Considerations Emerging from the Use of AI Tools to Identify Research Integrity Concerns This is the second 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. The advent of AI-based screening tools to detect research integrity concerns has added unique challenges to assessing and addressing research misconduct claims, including: Volume. The ease of access to and use of AI-based screening tools can lead to a high volume of claims, sometimes in public forums. Perspective. Claimants may lack connection to the lab, institution authors, or subject matter. Discernment. Claims can fail to account for human judgment, poor image quality, or historical practices. False Positives. AI-based screening tools can produce false positive results, which can be distracting and consume institutional resources. False Negatives. AI-based screening can also miss potential research integrity issues, which may lead to authors’ misplaced confidence in work or overreliance on the tools. Whistleblowers. Monetary incentives can lead to suits under the False Claims Act, which may move faster than internal research integrity reviews.