Evidence map›Paper›PMID 35679118›Full record

ArticleJMIR research protocols2022

Ageism and Artificial Intelligence: Protocol for a Scoping Review.

Charlene H Chu, Kathleen Leslie, Jiamin Shi, Rune Nyrup, Andria Bianchi, Shehroz S Khan, Samira Abbasgholizadeh Rahimi, Alexandra Lyn, Amanda Grenier

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JMIR research protocols, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Charlene H ChuLawrence S Bloomberg Faculty of Nursing, University of Toronto, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-0333-7210
Kathleen LeslieFaculty of Health Disciplines, Athabasca University, Athabasca, AB, Canada.ORCID https://orcid.org/0000-0003-0581-126X
Jiamin ShiLawrence S Bloomberg Faculty of Nursing, University of Toronto, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-1187-1990
Rune NyrupLeverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, United Kingdom.ORCID https://orcid.org/0000-0002-9880-6912
Andria BianchiKITE Research Institute - Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0003-3802-0771
Shehroz S KhanKITE Research Institute - Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-1195-4999
Samira Abbasgholizadeh RahimiDepartment of Family Medicine, McGill University, Montreal, QC, Canada.ORCID https://orcid.org/0000-0003-3781-1360
Alexandra LynFaculty of Health Disciplines, Athabasca University, Athabasca, AB, Canada.ORCID https://orcid.org/0000-0001-7377-8380
Amanda GrenierInstitute for Life Course and Aging, University of Toronto, Toronto, ON, Canada.ORCID https://orcid.org/0000-0003-2251-6035

Funding

Wellcome Trust
6 · The paper itself

Abstract

backgroundArtificial 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.

objectiveThis 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.

methodsThe 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.

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

conclusionsThe 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.

trial registrationOSF Registries AMG5P; https://osf.io/amg5p. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/33211.

Indexed as

ageismage-related biasesalgorithmsartificial intelligenceethicsgerontologyhealth databasehuman rightssearch strategy

Identifiers

PMID35679118
PMCPMC9227654

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.