Abstract：
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.
BACKGROUND: OSF Registries AMG5P; https://osf.io/amg5p.
UNASSIGNED: DERR1-10.2196/33211.
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.
BACKGROUND: OSF Registries AMG5P; https://osf.io/amg5p.
UNASSIGNED: DERR1-10.2196/33211.
摘要：
背景：人工智能（AI）已成为21世纪技术发展的主要驱动力，然而，人们很少关注算法对老年人的偏见。
目的：本文记录了范围审查的搜索策略和过程，探讨了与年龄相关的偏见如何在AI系统中编码或放大，以及相应的法律和道德含义。
方法：范围审查遵循Arksey和O\'Malley开发的6阶段方法框架。已在6个数据库中建立了检索策略。我们将通过搜索灰色文献数据库来调查人工智能中年龄歧视的法律含义，有针对性的网站,和流行的搜索引擎，并使用迭代搜索策略。研究符合纳入标准，如果他们是英语，同行评审，以电子方式提供全文，并满足以下两个附加标准之一：（1）在任何应用程序中包括与AI相关的“偏见”（例如，面部识别)和(2)讨论与老年或年龄歧视概念相关的偏见。至少有两名审稿人将独立进行标题，abstract,全文筛选。搜索结果将使用PRISMA-ScR（系统评论的首选报告项目和范围评论的Meta分析扩展）报告指南报告。我们将以结构化的形式绘制数据，并进行主题分析，以突出社会，legal,以及文献中报道的伦理含义。
结果：在2021年11月进行搜索时，数据库搜索产生了7595条记录。范围审查将于2022年12月完成。
结论：这些发现将为AI系统中与年龄相关的偏见程度提供跨学科的见解。研究结果将提供基础知识，鼓励多部门合作，以确保人工智能的开发和部署符合道德价值观和人权立法，因为它与老年人和老龄化人口有关。我们将在同行评审的期刊上发布评审结果，并通过研讨会和网络研讨会与利益相关者传播关键结果。
背景：OSF注册局AMG5P;https://osf.io/amg5p。
■DERR1-10.2196/33211。
目的：本文记录了范围审查的搜索策略和过程，探讨了与年龄相关的偏见如何在AI系统中编码或放大，以及相应的法律和道德含义。
方法：范围审查遵循Arksey和O\'Malley开发的6阶段方法框架。已在6个数据库中建立了检索策略。我们将通过搜索灰色文献数据库来调查人工智能中年龄歧视的法律含义，有针对性的网站,和流行的搜索引擎，并使用迭代搜索策略。研究符合纳入标准，如果他们是英语，同行评审，以电子方式提供全文，并满足以下两个附加标准之一：（1）在任何应用程序中包括与AI相关的“偏见”（例如，面部识别)和(2)讨论与老年或年龄歧视概念相关的偏见。至少有两名审稿人将独立进行标题，abstract,全文筛选。搜索结果将使用PRISMA-ScR（系统评论的首选报告项目和范围评论的Meta分析扩展）报告指南报告。我们将以结构化的形式绘制数据，并进行主题分析，以突出社会，legal,以及文献中报道的伦理含义。
结果：在2021年11月进行搜索时，数据库搜索产生了7595条记录。范围审查将于2022年12月完成。
结论：这些发现将为AI系统中与年龄相关的偏见程度提供跨学科的见解。研究结果将提供基础知识，鼓励多部门合作，以确保人工智能的开发和部署符合道德价值观和人权立法，因为它与老年人和老龄化人口有关。我们将在同行评审的期刊上发布评审结果，并通过研讨会和网络研讨会与利益相关者传播关键结果。
背景：OSF注册局AMG5P;https://osf.io/amg5p。
■DERR1-10.2196/33211。
