Beyond Detection: Ethical Foundations for Automated Dyslexic Error Attribution
Dyslexic spelling errors exhibit systematic phonological and orthographic patterns that distinguish them from the errors produced by typically developing writers. While this observation has motivated dyslexic-specific spell-checking and assistive writing tools, prior work has focused predominantly on error correction rather than attribution, and has largely neglected the ethical risks. The risk of harmful labelling, covert screening, algorithmic bias, and institutional misuse that automated classification of learners entails requires the development of robust ethical and legal frameworks for research in this area. This paper addresses both gaps. We formulate dyslexic error attribution as ...
| Authors | Samuel Rose, Debarati Chakraborty |
|---|---|
| Primary category | cs.CL |
| Published | 2 April 2026 |
| Last updated at source | 2 April 2026 |
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