Crafting Disability Fairness Learning in Data Science: A Student-Centric Pedagogical Approach
Abstract
Ensuring the fairness of machine learning (ML) systems for individuals with disabilities is crucial. Proactive measures are required to identify and mitigate biases in data and models, thereby preventing potential harm or bias against people with disabilities. While previous research on ML fairness education primarily concentrated on gender and race fairness, the domain of disability fairness has received comparatively little attention. Addressing this gap, we adopted a student-centric approach to craft a disability fairness teaching intervention. A focus group of students experienced in ML and accessible computing underscored the significance of engagement and scaffolding strategies for effectively learning intricate topics. Consequently, we crafted a disability fairness hands-on programming assignment that delves into uncovering disability bias with a lens that takes intersectionality into account. The assignment was tailored for an introductory undergraduate data science (DS) course. We employed reflective questions and surveys to gauge the effectiveness of our approach. The findings indicate the success of our approach in promoting a deeper understanding of disability fairness within the context of DS education.
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Only one paper highlighted the unique expertise disabled people bring to accessibility education, positioning them as both knowledge sources and active participants contributing to and learning from the intervention.
“Only one paper highlighted the unique expertise disabled people bring to accessibility education, positioning them as both knowledge sources and active participants contributing to and learning from the intervention [37].”
From Where are the Disabled Students?: A Literature Review of Disability Inclusion in Computing Education Research (2026) · cites this paper as [37]
These three papers stood out due to the depth of their engagement with the disabled communities they targeted and for how they situated accessibility as an issue of power, identity, and justice, rather than solely one of technical compliance or awareness.
“These papers stood out due to the depth of their engagement with the disabled communities they targeted [22, 29] and for how they situated accessibility as an issue of power, identity, and justice [22, 37], rather than solely one of technical compliance or awareness.”
From Where are the Disabled Students?: A Literature Review of Disability Inclusion in Computing Education Research (2026) · cites this paper as [37]
Only three papers in the dataset engaged with disability scholarship as a theoretical framing, specifically critical Disability Studies framings such as a social model of disability approach or a disability justice approach.
“Three papers explicitly engaged with critical Disability Studies framings, specifically taking a social model of disability approach [22, 29] or disability justice approach [37].”
From Where are the Disabled Students?: A Literature Review of Disability Inclusion in Computing Education Research (2026) · cites this paper as [37]
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References 31 · Cited by 3
- Authors
- Newman, P., Opdahl, T., Liu, Y., Wehrwein, S., & Elglaly, Y. N.
- Affiliations
- Western Washington University (US)
- Venue
- Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1
- Publisher
- ACM
- Published
- 2024-03-07
- Pages
- 944-950
- OpenAlex
- W4392564820
- Cited by
- 3
- References
- 31
- Citations by year
- 2026: 3
- FWCI
- 1.37
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Technical Symposium on Computer Science Education (SIGCSE) (publishedVersion)
- Funders
- SIGCSE Special Projects Grant (56004A)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Mathematics › Statistics and Probability › Statistics Education and Methodologies
- Keywords
- algorithmic fairness, machine learning bias, accessibility, intersectionality, data science education, scaffolding, accessible computing
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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