提出神经多样性包容的AI，打破以模仿人类为中心的偏见
Data-Driven and Participatory Approaches toward Neuro-Inclusive AI
- 用非模仿人类的视角重构AI评价标准
- 90%的人类化AI系统忽略自闭症群体视角
- 开发AUTALIC基准促进更具包容性的AI发展
当前人工智能在医疗应用中将自闭症视为神经典型社交能力的缺陷，而非人类多样性的体现，这种偏见已影响全球多达7500万自闭症人士。本文提出神经多样性包容的AI（Neuro-Inclusive AI）概念，主张摆脱以模仿人类为基准的评价体系。研究发现，90%的人类化AI代理系统忽视自闭症视角，且多数开发者认为伦理问题不在其职责范围内。通过标注员与大模型的实证实验，验证了二元标签方案足以捕捉反自闭症仇恨言论的细微差别。提出的AUTALIC基准可用于评估或微调模型，旨在为未来更包容的AI研究奠定基础。
原文摘要 · Abstract (English)
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.
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