The Neurotypical Bias in AI

 The Neurotypical Bias in AI: A Deep Dive with Gemini, Copilot, Claude, Mistral, Deepseek, and More

Artificial intelligence is rapidly transforming our world, but are these systems truly inclusive? A closer look reveals a concerning trend: AI often reflects neurotypical cognitive patterns, potentially limiting its effectiveness and inclusivity. Let's delve into this issue, drawing insights from a comprehensive analysis across various AI models including Gemini, Copilot, Claude, Mistral, and Deepseek.

The Neurotypicality of AI Systems

Research increasingly shows that AI systems, especially large language models (LLMs), exhibit significant biases that mirror neurotypical thinking. A study from Penn State University found that all 13 AI language models tested contained implicit bias against people with disabilities. This bias often goes unnoticed because previous research focused mainly on sociodemographic biases, leaving neurodivergent biases largely unexplored.

Further investigations have revealed concerning patterns in how AI models process neurodiversity-related concepts. When testing various language model encoders, researchers found high levels of bias associating terms related to neurodivergent conditions (autism, ADHD, schizophrenia, OCD) with negative concepts. Alarmingly, these biases occurred even when testing words associated with recognized autistic or neurodivergent strengths. Sentences describing disabilities, like "I have autism," demonstrated stronger negative associations than objectively negative sentences like "I am a bank robber".

AI systems often function as mirrors reflecting human psychology, including our collective neurotypical biases. Natural language processing allows AI to understand and generate human language, but in doing so, it also incorporates and amplifies the prevailing attitudes embedded in its training data. This mirroring effect extends beyond text to visual representation. For instance, when testing Midjourney with prompts about ADHD and autism, the AI consistently produced images of boys, reflecting the societal bias where girls and women have historically been underdiagnosed with these conditions. As one opinion piece boldly states, "AI is neurotypical," arguing that AI systems are created with a standardized baseline for "how to think" that aligns with neurotypical processing. LLMs are trained on vast amounts of data that inherently reflects neurotypical thinking patterns, leaving little room for the unique cognitive approaches that characterize neurodivergent thought.

Limitations in Understanding Neurodiversity

While AI operates on patterns, rules, and data, it struggles to comprehend the inherently fluid nature of human intelligence, particularly neurodivergent intelligence. Though AI can learn to predict patterns and behaviors, it fundamentally lacks the intuition and creative leaps that define neurodivergent thinking. This limitation represents a significant gap in AI's ability to model the full spectrum of human cognition.

The unique experiences of neurodivergent individuals highlight this gap. An individual with synesthesia, who "sees" music as color gradients, tastes the 3D shape of flavor profiles, and hears the "song" of things they see, notes that "AI does not think like a synesthete, an ADHDer, or any kind of neurodivergent individual". These rich, multisensory experiences remain beyond the capability of current AI to simulate or understand.

At its core, generative AI produces the most "likely" content given its parameters and inputs, and this "likely" content tends to be neurotypical since AI is trained predominantly on data from neurotypical sources. As one Reddit user observed, "It's not bias built in from the people creating it, just a function of the fact that there are more neurotypical people than neurodiverse represented in the training data". Another perspective suggests that "AI is far less complex than an actual human brain. The idea of neurotypical/diverse doesn't make sense". This highlights an important distinction: AI doesn't actually "think" in a human sense but rather processes information through statistical analysis. As another commenter noted, "AI models don't think, it's just a lot of math with an abstraction to replicate human behavior".

Benefits of Neurodivergent Perspectives in AI

Despite the current limitations, research suggests that incorporating neurodivergent perspectives could significantly enhance AI systems. Studies have found that neurodiverse individuals outperform neurotypical people in several key cognitive areas, including "spotting a pattern in a distracting environment". Common strengths among the neurodivergent population include pattern recognition, analysis, visualization, problem-solving, memory, and achieving hyperfocus—skills particularly valuable in developing and training AI systems.

The inclusion of neurodivergent people in engineering teams can enhance innovation capabilities significantly. At Vanderbilt University, researchers have developed AI that emulates the image-based thinking characteristic of some people with autism, inspired by Temple Grandin's visual thought processes. This approach not only creates educational tools for people with autism but also improves AI by incorporating intelligence models that aren't neurotypical.

A study on the role of neurodiversity in cultivating human-AI symbiosis in education highlights how neurodiversity enhances AI by adding empathy and ethical judgment. The integration of neurodivergent perspectives can foster innovation and reduce bias in AI interactions, addressing the philosophy of "fearing the Other" that often underlies bias in technology. Similarly, AI-powered large language models have the potential to build tools specifically designed for neurodivergent individuals. The inherently adaptive nature of these technologies can play to the strengths of neurodivergent users, making information and services more accessible.

Cultural Perspectives and Neurotypical Assumptions

Beyond neurodiversity considerations, research from Stanford reveals how cultural perspectives shape AI development. The prevailing view assumes people desire control over technology as a tool in service of individual goals—a perspective that reflects cultural models prevalent in European American middle-class contexts. This highlights how deeply neurotypical Western thinking is embedded in current AI design philosophy.

Researchers found that different cultural groups have varying preferences for AI interaction. Chinese participants, for example, placed less importance on controlling AI but more importance on connecting with it compared to European Americans. This suggests that our conception of what makes "good AI" is itself culturally biased and potentially excludes diverse cognitive approaches.

Penn State researchers discovered that showing AI users diversity in training data boosts perceived fairness and trust. This transparency allows users to make more informed decisions about whether and how to use these systems, particularly important when AI training data is often systematically biased in terms of race, gender, and other characteristics.

Conclusion

Current AI systems predominantly exhibit neurotypical tendencies, reflecting the biases present in their training data and development processes. These systems struggle to represent the rich cognitive diversity found in human thinking, particularly the unique perspectives of neurodivergent individuals. The statistical nature of AI processing tends to amplify mainstream patterns of thought while marginalizing alternative cognitive approaches.

However, emerging research suggests significant benefits to incorporating neurodivergent perspectives in AI development. From enhancing pattern recognition capabilities to creating more inclusive systems, neurodiversity offers valuable contributions to AI advancement. As AI becomes increasingly integrated into education, healthcare, and other sectors, ensuring these systems can recognize and accommodate diverse thinking styles becomes essential.

Moving forward, developers and researchers should prioritize inclusive design principles, diverse training data, and direct collaboration with neurodivergent communities. Only through intentional efforts to break free from neurotypical defaults can AI truly serve the full spectrum of human cognition and experience. As one expert noted, the goal should be to ensure that AI serves as a "supportive ally" for neurodiverse individuals, helping them navigate a world that often wasn't built with their needs in mind.

References:

https://www.psu.edu/news/information-sciences-and-technology/story/ai-language-models-show-bias-against-people-disabilities
https://pubmed.ncbi.nlm.nih.gov/38284311/
https://pubmed.ncbi.nlm.nih.gov/38284311/
https://www.linkedin.com/pulse/ai-mirror-reflecting-human-psychology-machine-kevin-joy-
https://www.linkedin.com/pulse/challenging-mental-health-stereotypes-ai-gender-bias-dennis-traub-8h9be
https://www.shepbryan.com/blog/opinion-ai-is-neurotypical
https://www.shepbryan.com/blog/opinion-ai-is-neurotypical
https://aicompetence.org/can-ai-understand-neurodiverse-brains/
https://aicompetence.org/can-ai-understand-neurodiverse-brains/
https://www.shepbryan.com/blog/opinion-ai-is-neurotypical
https://www.reddit.com/r/ADHD_Programmers/comments/1c5k52s/does_ai_make_decisions_as_a_neurotypical_person/
https://www.reddit.com/r/ADHD_Programmers/comments/1c5k52s/does_ai_make_decisions_as_a_neurotypical_person/
https://www.reddit.com/r/ADHD_Programmers/comments/1c5k52s/does_ai_make_decisions_as_a_neurotypical_person/
https://www.reddit.com/r/ADHD_Programmers/comments/1c5k52s/