Lawrence Bloomberg Faculty of Nursing, University of Toronto, Toronto, ON, Canada.
Knowledge, Innovation, Talent, Everywhere (KITE), Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.
JMIR Aging. 2024 Mar 22;7:e53564. doi: 10.2196/53564.
Research suggests that digital ageism, that is, age-related bias, is present in the development and deployment of machine learning (ML) models. Despite the recognition of the importance of this problem, there is a lack of research that specifically examines the strategies used to mitigate age-related bias in ML models and the effectiveness of these strategies.
To address this gap, we conducted a scoping review of mitigation strategies to reduce age-related bias in ML.
We followed a scoping review methodology framework developed by Arksey and O'Malley. The search was developed in conjunction with an information specialist and conducted in 6 electronic databases (IEEE Xplore, Scopus, Web of Science, CINAHL, EMBASE, and the ACM digital library), as well as 2 additional gray literature databases (OpenGrey and Grey Literature Report).
We identified 8 publications that attempted to mitigate age-related bias in ML approaches. Age-related bias was introduced primarily due to a lack of representation of older adults in the data. Efforts to mitigate bias were categorized into one of three approaches: (1) creating a more balanced data set, (2) augmenting and supplementing their data, and (3) modifying the algorithm directly to achieve a more balanced result.
Identifying and mitigating related biases in ML models is critical to fostering fairness, equity, inclusion, and social benefits. Our analysis underscores the ongoing need for rigorous research and the development of effective mitigation approaches to address digital ageism, ensuring that ML systems are used in a way that upholds the interests of all individuals.
Open Science Framework AMG5P; https://osf.io/amg5p.
研究表明，数字年龄歧视，即与年龄相关的偏见，存在于机器学习 (ML) 模型的开发和部署中。尽管认识到这个问题的重要性，但缺乏专门研究用于减轻 ML 模型中与年龄相关的偏见的策略以及这些策略的有效性的研究。
为了解决这一差距，我们对减轻 ML 中与年龄相关的偏见的策略进行了范围综述。
我们遵循了由 Arksey 和 O'Malley 开发的范围综述方法框架。该搜索是与信息专家共同开发的，并在 6 个电子数据库（IEEE Xplore、Scopus、Web of Science、CINAHL、EMBASE 和 ACM 数字图书馆）以及 2 个额外的灰色文献数据库（OpenGrey 和 Grey Literature Report）中进行。
我们确定了 8 篇试图减轻 ML 方法中与年龄相关的偏见的出版物。与年龄相关的偏见主要是由于数据中老年人代表性不足而引入的。减轻偏见的努力分为以下三种方法之一：（1）创建更平衡的数据集，（2）扩充和补充数据，（3）直接修改算法以实现更平衡的结果。
在 ML 模型中识别和减轻相关偏见对于促进公平、公正、包容和社会效益至关重要。我们的分析强调了进行严格研究和开发有效缓解方法以解决数字年龄歧视的持续需求，以确保 ML 系统的使用符合所有个人的利益。
开放科学框架 AMG5P；https://osf.io/amg5p。
