Algorithmic Exclusion: A Conceptual Model of How Artificial Intelligence Systems Disable, from Training Data to Deployment
DOI:
https://doi.org/10.68854/c9pnn919Keywords:
algorithmic bias, disability, accessibility, fairness, speech recognition, facial analysis, automated hiring, inclusive design, AI procurement, AfricaAbstract
The literature on fairness in artificial intelligence has organised itself around race and gender. Disability, by contrast, is rarely treated as a fairness category at all, even though disabled people are among the groups most exposed to algorithmic harm: speech recognisers that cannot transcribe dysarthric speech, emotion classifiers built on a norm of expressiveness that autistic people do not share, hiring filters that penalise employment gaps, and proctoring software built on assumptions about how a test-taker's body and circumstances should look. This conceptual paper asks how AI systems disable, and why the standard fairness toolkit answers so poorly when they do. Drawing on the social model of disability and critical disability studies, on the algorithmic-fairness scholarship and its legal and feminist critiques, and on peer-reviewed research published between 2023 and 2026 on the performance of speech, facial, visual, language, hiring and screening systems for disabled users, the paper develops a four-mechanism model of algorithmic exclusion: under-representation in training data, normative modelling that treats atypical bodies and behaviours as noise or risk, inaccessible interfaces to the system itself, and deployment contexts that offer no human alternative. It then argues that disability defeats group-based fairness remedies for four structural reasons, namely heterogeneity, invisibility, legally protected non-disclosure and poor fit with the categorical variables on which parity metrics depend. Eight propositions locate where exclusion is most probable and most damaging, with particular attention to African settings where disability data are scarcest and where regulatory capacity to demand accessible AI is thinnest. The paper closes with a design-and-procurement framework that treats disability as a first-order constraint rather than an edge case, and with recommendations for developers, public purchasers, data-protection authorities, organisations of persons with disabilities and the speech-technology and fairness research communities.
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