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Description: Mapping public trust in nurses with dimensions of trustworthy artificial intelligence: A scoping review OBJECTIVES The goal of this study is to map and synthesize the evidence on dimensions of trust that are perceived by patients to be important in their relationships and/or interactions with nurses in order to inform and envision novel approaches to developing trustworthy artificial intelligence (AI) applications. The overarching aim is to leverage the longstanding public trust that nurses are perceived to hold and develop insight into what features of trustworthiness, if any, could potentially be demonstrated by artificial intelligence models. Achieving the goal of the study will require completion of the following objectives, to: i) scope and synthesize current knowledge from the nursing trust literature to generate new insights into important human dimensions of trust in relation to nurses’ practice; ii) map and compare understandings of human and computational AI and understandings of trustworthiness; iii) identify and prioritize knowledge and research gaps and recommend future research directions for development of trustworthy AI; and iv) synthesize results and develop research outputs in formats that will be readily accessible and contextually relevant for researchers and knowledge users. METHODS The updated Joanna Briggs Institute (JBI) methodology for scoping reviews (Peters et al., 2020) that incorporates enhancements by Levac et al. (2010) and provides methodological clarity in response to criticism to Arksey and O’Malley’s (2005) approach will be used. In adherence to this methodology and to increase the transparency in the process, the protocol for the scoping review will be developed and registered through the Open Science Framework. A draft Cumulative Index to Nursing and Allied Health Literature (CINAHL) search strategy was developed which combined the concepts of nursing and trust. The search strategy was developed and conducted by a nursing specialist librarian and information specialist. Final refinement of the search strategy was conducted through the Peer Review of Electronic Search Strategies (PRESS) guidelines (McGowan et al., 2016) where a librarian external to the team reviewed the search strategy. Feedback from the PRESS process were incorporated into the search strategy. Databases searched include CINAHL, EMBASE, MEDLINE, PsycINFO, and forward and backward citation searching. The searches were conducted in October 2022. Results from the completed searches will be uploaded into the Covidence literature synthesis software to remove duplicates and screen articles. Screening and selection will adhere to the pre-specified inclusion and exclusion criteria outlined in the study protocol (e.g., exclusion of grey literature). A minimum of two researchers will review all titles and abstracts for relevance and inclusion. Included articles will proceed to full-article review and further evaluation of inclusion. Any disagreements will be reviewed with the PI and resolved through consensus. Data extraction will be completed by two researchers using a piloted data extraction form including standard categories (e.g., author(s), year of publication, study aims/purpose, population and sample size (if applicable), methodology and methods used, etc.) and key findings relating to the scoping review question (Peters et al., 2020). A thematic analysis approach and iterative synthesis of findings by the team will be used to guide the collation and summarization of findings in order to identify themes - an adaptation of the approach by Crampton et al. (2016). First, a minimum of two researchers will read and annotate each article using computational dimensions of trustworthy AI as identified by Liu et al. (2021) (i.e., safety & robustness, non-discrimination & fairness, explainability, privacy, accountability & auditability, and environmental well-being). Where there is uncertainty in whether a dimension of trustworthiness is captured within existing categories and/or whether a dimension fits under more than one category, the PI will discuss with the team and will make a decision on how the dimension will be categorized and whether any new dimensions need to be named. Next, the team will review all of the articles, compare, and finalize the annotations of trustworthiness dimensions as they correspond with the articles. Points of disagreement will be discussed as a team until a consensus is reached. RESULTS The scoping review and corresponding academic manuscript will be completed in 18 months (est. April 2024). Research team meetings will be held at regular intervals, including bringing together interdisciplinary collaborators at key points during the study. The results will describe where research has been done, what AI technologies have been developed and studied, how these technologies have been evaluated and how nurses have participated, and how ethical issues have been addressed in the research. CONCLUSION Research findings will contribute to the larger vision of developing trustworthy AI in health and social systems and enrich understandings of dimensions of trust that may have computational parallels for trustworthy AI systems. References Peters, M. D., Marnie, C., Tricco, A. C., Pollock, D., Munn, Z., Alexander, L., ... & Khalil, H. (2020). Updated methodological guidance for the conduct of scoping reviews. JBI evidence synthesis, 18(10), 2119-2126. Levac, D., Colquhoun, H., & O'Brien, K. K. (2010). Scoping studies: advancing the methodology. Implementation science, 5(1), 1-9. Arksey, H., & O'Malley, L. (2005). Scoping studies: towards a methodological framework. International journal of social research methodology, 8(1), 19-32. McGowan, J., Sampson, M., Salzwedel, D. M., Cogo, E., Foerster, V., & Lefebvre, C. (2016). PRESS peer review of electronic search strategies: 2015 guideline statement. Journal of clinical epidemiology, 75, 40-46. Crampton, N. H., Reis, S., & Shachak, A. (2016). Computers in the clinical encounter: a scoping review and thematic analysis. Journal of the American Medical Informatics Association, 23(3), 654-665. Liu, H., Wang, Y., Fan, W., Liu, X., Li, Y., Jain, S., ... & Tang, J. (2021). Trustworthy AI: A computational perspective. arXiv preprint arXiv:2107.06641.