JMIR Publications

ABSTRACT

Background:

Research suggests that digital ageism i.e., age-related bias, in artificial intelligence (AI) is present in the development and deployment of machine learning models. Despite recognizing the importance of this problem, there is a lack of research that specifically examines the strategies used to mitigate age-related bias in machine learning models and the effectiveness of these strategies.

Objective:

To address this gap, a scoping review of mitigation strategies to reduce age-related bias in AI was conducted.

Methods:

Methods:

A scoping review methodology framework developed by Arksey and O’Malley was followed. The search was developed in conjunction with an information specialist, and conducted in six electronic databases (IEEE Xplore, Scopus, Web of Science, CINAHL, EMBASE, and ACM digital library) as well as two additional grey literature databases (OpenGrey and Grey Literature Report).

Results:

Results:

We identified eight publications that attempted to mitigate age-related bias. Age-related bias was introduced primarily from 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 dataset, 2) augmenting and supplementing their data, and 3) modifying the algorithm directly to achieve a more balanced result.

Conclusions:

Implications: Identifying and mitigating-related biases in AI systems are critical to fostering fairness, equity, inclusion and social benefits. Our analysis underscores the ongoing need for rigorous research and development of effective mitigation approaches to address digital ageism, ensuring that AI systems are used in a way that upholds the interests of all individuals.