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Volume 2, Issue 2

Closing the Distance: Explainable AI for Diagnosing Neurotypicality in Disability Assessors

Axel Andersen10 pages188 KB

Abstract

This paper proposes a complex machine learning framework to diagnose high-risk neurotypicality in clinicians, bureaucrats, data scientists and 'innovation leaders' involved in algorithmic disability assessment. We hypothesise that the urge to algorithmically contain disabled people functions as a psychological distancing technology—allowing professionals to comply with policy without risking actual relationship. Our TabPFN-NeuroMix model outputs a Neurotypicality Proximity Avoidance Index (NPAI) with explainability via SHAP, repurposed as Shapley Ableism Partitioning.

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