Data-Driven and Participatory Approaches toward Neuro-Inclusive AI

TL;DR

This paper advocates for neuro-inclusive AI by analyzing biases against autistic perspectives, proposing new datasets and benchmarks, and demonstrating that inclusive labeling schemes can better represent neurodiversity in AI systems.

Contribution

It introduces the concept of neuro-inclusive AI, analyzes anti-autistic biases, and develops the AUTALIC benchmark to evaluate and improve neurodiversity representation in AI.

Findings

90% of human-like AI agents exclude autistic perspectives

Binary labeling schemes effectively capture anti-autistic hate speech nuances

The AUTALIC benchmark provides a foundation for neuro-inclusive AI evaluation

Abstract

Biased data representation in AI marginalizes up to 75 million autistic people worldwide through medical applications viewing autism as a deficit of neurotypical social skills rather than an aspect of human diversity, and this perspective is grounded in research questioning the humanity of autistic people. Turing defined artificial intelligence as the ability to mimic human communication, and as AI development increasingly focuses on human-like agents, this benchmark remains popular. In contrast, we define Neuro-Inclusive AI as datasets and systems that move away from mimicking humanness as a benchmark for machine intelligence. Then, we explore the origins, prevalence, and impact of anti-autistic biases in current research. Our work finds that 90% of human-like AI agents exclude autistic perspectives, and AI creators continue to believe ethical considerations are beyond the scope of their work. To improve the autistic representation in data, we conduct empirical experiments with annotators and LLMs, finding that binary labeling schemes sufficiently capture the nuances of labeling anti-autistic hate speech. Our benchmark, AUTALIC, can be used to evaluate or fine-tune models, and was developed to serve as a foundation for more neuro-inclusive future work.