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TRUVACE RECORD VERSION
record: TRV-2026-1374
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-14T22:02:38.102463Z
status: published
lens: p_space
sector: labor
headline: Artificial intelligence and disability in the workplace: A systematic review of ethical issues
dek: BackgroundThe use of artificial intelligence (AI) in workplace disability contexts is expanding, raising important ethical concerns. Existing frameworks, such as the OECD Framework for the Classification of AI Systems, provide structure for understanding AI applications but lack an explicit focus on ethics in relation to disability.ObjectiveThis systematic review examines ethical issues arising from the use of AI for employees with disabilities and aims to interpret the OECD framework through an explicit ethics…
gain_title: (none)
problem_title: Across 23 reviewed studies, workplace AI raises recurring ethical risks for employees with disabilities, including bias, privacy and consent problems, discrimination, and weak governance.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Across 23 reviewed studies, workplace AI raises recurring ethical risks for employees with disabilities, including bias, privacy and consent problems, discrimination, and weak governance.
problem_evidence: discrimination and inequitable access to opportunities
quick_read: Researchers carried out a PRISMA-guided systematic review of 23 peer-reviewed studies on AI used with employees who have disabilities. They extended the OECD framework for classifying AI systems into five domains (context, people, data and input, AI model, and task and output) and read the evidence through an ethics lens.

The review points to ethical risks that depend on context and weigh more heavily on disabled workers, particularly where data are biased or incomplete and models are opaque. The authors call for audits, inclusive testing and worker-centered governance, but the abstract does not report whether these measures work in practice.
limitation: The abstract describes ethical risks as context-dependent and presents the review as a synthesis of current evidence. It does not report how well the recommended safeguards perform in practice.
tag: Evidence-backed problem
key_points: The review covered 23 peer-reviewed studies identified through PRISMA-guided searches. | Four recurring concerns emerged: automation bias, data privacy and informed consent, discrimination and inequitable access, and AI governance gaps. | The authors built a five-domain framework by extending the OECD AI classification framework with an ethics focus. | Recommended actions include algorithmic audits, inclusive testing, transparent data practices, interdisciplinary oversight and worker-centered governance.
rundown: The work starts from the observation that AI use in workplace disability contexts is expanding, while existing frameworks such as the OECD classification offer structure but no explicit ethics focus on disability.

The authors' five-domain framework covers Context, People, Data & Input, AI Model, and Task & Output. Ethical issues were analyzed within each domain rather than treated as a separate add-on.

The identified risks were described as context-dependent and made worse by biased or incomplete data and opaque AI models. The proposed directions for research and practice cover testing, data transparency, oversight and worker involvement in governance.
sources:
- peer_reviewed | WORK: A Journal of Prevention, Assessment & Rehabilitation | https://doi.org/10.1177/10519815261491693 | 2026-10-13
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