# "I Hadn't Thought About That": Creators of Human-like AI Weigh in on Ethics And Neurodivergence

arXiv: https://arxiv.org/abs/2506.12098 · HTML: https://mlsanity.com/paper/2506.12098/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 102 references that are in the map.
- First posted on arXiv 12 Jun 2025.

## Builds on (most cited first, 11 of 11)

- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Does GPT-4 pass the Turing test?](https://mlsanity.com/paper/2310.20216/) (2023)
- [Identifying and Improving Disability Bias in GPT-Based Resume Screening](https://mlsanity.com/paper/2402.01732/) (2024)
- [Artificial intelligence in communication impacts language and social relationships](https://mlsanity.com/paper/2102.05756/) (2021)
- [Bias and Fairness in Chatbots: An Overview](https://mlsanity.com/paper/2309.08836/) (2023)
- [Algorithmic Injustices: Towards a Relational Ethics](https://mlsanity.com/paper/1912.07376/) (2019)
- [Tackling Algorithmic Disability Discrimination in the Hiring Process: An Ethical, Legal and Technical Analysis](https://mlsanity.com/paper/2206.06149/) (2022)
- [Listening to the Voices: Describing Ethical Caveats of Conversational User Interfaces According to Experts and Frequent Users](https://mlsanity.com/paper/2401.14746/) (2024)
- [Visions of a Discipline: Analyzing Introductory AI Courses on YouTube](https://mlsanity.com/paper/2407.13077/) (2024)
- [Who Puts the "Social" in "Social Computing"?: Using A Neurodiversity Framing to Review Social Computing Research](https://mlsanity.com/paper/2410.15527/) (2024)
- [The Social Construction of Visualizations: Practitioner Challenges and Experiences of Visualizing Race and Gender Demographic Data](https://mlsanity.com/paper/2502.09048/) (2025)

## Built on by (most cited first, 1 of 1)

- [A Scoping Review of the Ethical Perspectives on Anthropomorphising Large Language Model-Based Conversational Agents](https://mlsanity.com/paper/2601.09869/) (2026)

## Abstract (arXiv)

Human-like AI agents such as robots and chatbots are becoming increasingly popular, but they present a variety of ethical concerns. The first concern is in how we define humanness, and how our definition impacts communities historically dehumanized by scientific research. Autistic people in particular have been dehumanized by being compared to robots, making it even more important to ensure this marginalization is not reproduced by AI that may promote neuronormative social behaviors. Second, the ubiquitous use of these agents raises concerns surrounding model biases and accessibility. In our work, we investigate the experiences of the people who build and design these technologies to gain insights into their understanding and acceptance of neurodivergence, and the challenges in making their work more accessible to users with diverse needs. Even though neurodivergent individuals are often marginalized for their unique communication styles, nearly all participants overlooked the conclusions their end-users and other AI system makers may draw about communication norms from the implementation and interpretation of humanness applied in participants' work. This highlights a major gap in their broader ethical considerations, compounded by some participants' neuronormative assumptions about the behaviors and traits that distinguish "humans" from "bots" and the replication of these assumptions in their work. We examine the impact this may have on autism inclusion in society and provide recommendations for additional systemic changes towards more ethical research directions.