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    Appendix for Principles for Responsible AI in Medical School and Residency Selection

    The following journal articles, organizational resources, and technical references support the Principles for Responsible AI developed by the AAMC and the AI in Admissions and Selection Technical Advisory Committee.

    Additional Relevant Journal Articles

    Drum B, Shi J, Peterson B, Lamb S, Hurdle JF, Gradick C. Using natural language processing and machine learning to identify internal medicine-pediatrics residency values in applications. Acad Med. 2023;98(11):1278-1282. doi: 10.1097/ACM.0000000000005352 

    Knopp MI, Warm, EJ, Weber, et al. AI-enabled medical education: AI enabled medical education: Threads of change, promising futures, and risky realities across four potential future worlds. JMIR Med Educ. 2023;9. doi: 10.2196/50373 

    Mahtani AU, Reinstein I, Marin M, Burk-Rafel J. A new tool for holistic residency application review: Using natural language processing of applicant experiences to predict interview invitation. Acad Med. 2023;98(9):1018-1021. doi: 10.1097/ACM.0000000000005210 

    Triola MM, Reinstein I, Marin M, et al. Artificial intelligence screening of medical school applications: Development and validation of a machine-learning algorithm. Acad Med. 2023;98(9):1036-1043. doi: 10.1097/ACM.0000000000005202 

    Zhang N, Wang M, Xu H, Koenig N, Hickman L. Reducing subgroup differences in personnel selection through the application of machine learning. Pers Psychol. 2023;76(4):1125-1159. doi: 10.1111/peps.12593

    Organizational Resources

    Association of Test Publishers. Artificial intelligence and the testing industry: A primer. Published July 6, 2021. https://www.testpublishers.org/assets/ATP%20White%20Paper_AI%20and%20Testing_A%20Primer_6July2021_Final%20R1%20.pdf

    Department of Education Office of Educational Technology. Artificial intelligence and the future of teaching and learning: Insights and recommendations. Published May 2023. https://tech.ed.gov/files/2023/05/ai-future-of-teaching-and-learning-report.pdf

    European Commission. Proposal for a Regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain Union legislative acts. COM/2021/206 final. Published April 21, 2021. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52021PC0206

    National Institute of Standards and Technology. Artificial Intelligence Risk Management Framework (AI RMF 1.0). Published January 2023. https://doi.org/10.6028/nist.ai.100-1

    Organisation for Economic Co-operation and Development. The OECD Artificial Intelligence Principles. Published May 2019. Updated May 2024. https://oecd.ai/en/ai-principles

    Society for Industrial and Organizational Psychology. Considerations and Recommendations for the Validation and Use of AI-based Assessments for Employee Selection. January 2023. https://www.siop.org/Portals/84/SIOP%20Considerations%20and%20Recommendations%20for%20the%20Validation%20and%20Use%20of%20AI-Based%20Assessments%20for%20Employee%20Selection%20010323.pdf?ver=5w576kFXzxLZNDMoJqdIMw%3d%3d 

    Stanford HAI. The 2019 AI Index Report. Published December 2019. https://hai.stanford.edu/research/ai-index-2019

    Technical References

    FAccT ’23: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency.

    Association for Computing Machinery; June 12-15, 2023; Chicago, IL. https://dl.acm.org/doi/proceedings/10.1145/3593013 

    Hooker S. Fairness, security, and governance in machine learning. Stanford University; 2022. https://docs.google.com/presentation/d/1cshMKKSX24L0RL7LNzyOkZNQHD7N-Zyff8iffrLIVYM/edit

    Montreal AI Ethics Institute. https://montrealethics.ai/

    Trustworthy Machine Learning. Resources. https://trustworthyml.org/resources