TY - JOUR
T1 - Ageism and Artificial Intelligence
T2 - Protocol for a Scoping Review
AU - Chu, Charlene H.
AU - Leslie, Kathleen
AU - Shi, Jiamin
AU - Nyrup, Rune
AU - Bianchi, Andria
AU - Khan, Shehroz S.
AU - Rahimi, Samira Abbasgholizadeh
AU - Lyn, Alexandra
AU - Grenier, Amanda
N1 - Publisher Copyright:
© 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.
PY - 2022/6/1
Y1 - 2022/6/1
N2 - Background: 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. Objective: 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. Methods: 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. Results: The database searches resulted in 7595 records when the searches were piloted in November 2021. The scoping review will be completed by December 2022. Conclusions: 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.
AB - Background: 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. Objective: 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. Methods: 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. Results: The database searches resulted in 7595 records when the searches were piloted in November 2021. The scoping review will be completed by December 2022. Conclusions: 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.
KW - age-related biases
KW - ageism
KW - algorithms
KW - artificial intelligence
KW - ethics
KW - gerontology
KW - health database
KW - human rights
KW - search strategy
UR - http://www.scopus.com/inward/record.url?scp=85132027927&partnerID=8YFLogxK
U2 - 10.2196/33211
DO - 10.2196/33211
M3 - Review article
AN - SCOPUS:85132027927
VL - 11
JO - JMIR Research Protocols
JF - JMIR Research Protocols
IS - 6
M1 - e33211
ER -
