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      Ageism and Artificial Intelligence: Protocol for a Scoping Review


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          Artificial intelligence (AI) has emerged as a major driver of technological development in the 21st century, yet little attention has been paid to algorithmic biases toward older adults.


          This paper documents the search strategy and process for a scoping review exploring how age-related bias is encoded or amplified in AI systems as well as the corresponding legal and ethical implications.


          The scoping review follows a 6-stage methodology framework developed by Arksey and O’Malley. The search strategy has been established in 6 databases. We will investigate the legal implications of ageism in AI by searching grey literature databases, targeted websites, and popular search engines and using an iterative search strategy. Studies meet the inclusion criteria if they are in English, peer-reviewed, available electronically in full text, and meet one of the following two additional criteria: (1) include “bias” related to AI in any application (eg, facial recognition) and (2) discuss bias related to the concept of old age or ageism. At least two reviewers will independently conduct the title, abstract, and full-text screening. Search results will be reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) reporting guideline. We will chart data on a structured form and conduct a thematic analysis to highlight the societal, legal, and ethical implications reported in the literature.


          The database searches resulted in 7595 records when the searches were piloted in November 2021. The scoping review will be completed by December 2022.


          The findings will provide interdisciplinary insights into the extent of age-related bias in AI systems. The results will contribute foundational knowledge that can encourage multisectoral cooperation to ensure that AI is developed and deployed in a manner consistent with ethical values and human rights legislation as it relates to an older and aging population. We will publish the review findings in peer-reviewed journals and disseminate the key results with stakeholders via workshops and webinars.

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          PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation

          Scoping reviews, a type of knowledge synthesis, follow a systematic approach to map evidence on a topic and identify main concepts, theories, sources, and knowledge gaps. Although more scoping reviews are being done, their methodological and reporting quality need improvement. This document presents the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) checklist and explanation. The checklist was developed by a 24-member expert panel and 2 research leads following published guidance from the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network. The final checklist contains 20 essential reporting items and 2 optional items. The authors provide a rationale and an example of good reporting for each item. The intent of the PRISMA-ScR is to help readers (including researchers, publishers, commissioners, policymakers, health care providers, guideline developers, and patients or consumers) develop a greater understanding of relevant terminology, core concepts, and key items to report for scoping reviews.
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            Scoping studies: towards a methodological framework

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              Scoping studies: advancing the methodology

              Background Scoping studies are an increasingly popular approach to reviewing health research evidence. In 2005, Arksey and O'Malley published the first methodological framework for conducting scoping studies. While this framework provides an excellent foundation for scoping study methodology, further clarifying and enhancing this framework will help support the consistency with which authors undertake and report scoping studies and may encourage researchers and clinicians to engage in this process. Discussion We build upon our experiences conducting three scoping studies using the Arksey and O'Malley methodology to propose recommendations that clarify and enhance each stage of the framework. Recommendations include: clarifying and linking the purpose and research question (stage one); balancing feasibility with breadth and comprehensiveness of the scoping process (stage two); using an iterative team approach to selecting studies (stage three) and extracting data (stage four); incorporating a numerical summary and qualitative thematic analysis, reporting results, and considering the implications of study findings to policy, practice, or research (stage five); and incorporating consultation with stakeholders as a required knowledge translation component of scoping study methodology (stage six). Lastly, we propose additional considerations for scoping study methodology in order to support the advancement, application and relevance of scoping studies in health research. Summary Specific recommendations to clarify and enhance this methodology are outlined for each stage of the Arksey and O'Malley framework. Continued debate and development about scoping study methodology will help to maximize the usefulness and rigor of scoping study findings within healthcare research and practice.

                Author and article information

                JMIR Res Protoc
                JMIR Res Protoc
                JMIR Research Protocols
                JMIR Publications (Toronto, Canada )
                June 2022
                9 June 2022
                : 11
                : 6
                : e33211
                [1 ] Lawrence S Bloomberg Faculty of Nursing University of Toronto Toronto, ON Canada
                [2 ] Institute for Life Course and Aging University of Toronto Toronto, ON Canada
                [3 ] KITE Research Institute - Toronto Rehabilitation Institute University Health Network Toronto, ON Canada
                [4 ] Faculty of Health Disciplines Athabasca University Athabasca, AB Canada
                [5 ] Canadian Health Workforce Network Ottawa, ON Canada
                [6 ] Dalla Lana School of Public Health University of Toronto Toronto, ON Canada
                [7 ] Leverhulme Centre for the Future of Intelligence University of Cambridge Cambridge United Kingdom
                [8 ] Department of Clinical and Organizational Ethics University Health Network Toronto, ON Canada
                [9 ] Institute of Biomedical Engineering University of Toronto Toronto, ON Canada
                [10 ] Department of Family Medicine McGill University Montreal, QC Canada
                [11 ] Mila - Quebec AI Institute Montreal, QC Canada
                [12 ] Lady Davis Institute for Medical Research Herzl Family Practice Centre Jewish General Hospital Montreal, QC Canada
                [13 ] Factor-Inwentash Faculty of Social Work University of Toronto Toronto, ON Canada
                [14 ] Baycrest Hospital Toronto, ON Canada
                Author notes
                Corresponding Author: Charlene H Chu charlene.chu@ 123456utoronto.ca
                Author information
                ©Charlene H Chu, Kathleen Leslie, Jiamin Shi, Rune Nyrup, Andria Bianchi, Shehroz S Khan, Samira Abbasgholizadeh Rahimi, Alexandra Lyn, Amanda Grenier. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 09.06.2022.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included.

                : 27 August 2021
                : 5 February 2022
                : 11 April 2022
                : 21 April 2022

                artificial intelligence,ageism,age-related biases,gerontology,algorithms,search strategy,health database,human rights,ethics


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