mlsanityData to 14 Oct 2026

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

arXiv 2506.12098 ↗First posted 12 Jun 2025cs.CY cs.AIcited by 1

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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.

Key facts. Cited by 1 paper 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

11 of 102 references.

References · 11

  1. 2018Improving fairness in machine learning systems: What do industry practitioners need?cited by 262
  2. 2023Does GPT-4 pass the Turing test?cited by 37
  3. 2024Identifying and Improving Disability Bias in GPT-Based Resume Screeningcited by 28
  4. 2021Artificial intelligence in communication impacts language and social relationshipscited by 22
  5. 2023Bias and Fairness in Chatbots: An Overviewcited by 20
  6. 2019Algorithmic Injustices: Towards a Relational Ethicscited by 11
  7. 2022Tackling Algorithmic Disability Discrimination in the Hiring Process: An Ethical, Legal and Technical Analysiscited by 10
  8. 2024Listening to the Voices: Describing Ethical Caveats of Conversational User Interfaces According to Experts and Frequent Userscited by 8
  9. 2024Visions of a Discipline: Analyzing Introductory AI Courses on YouTubecited by 3
  10. 2024Who Puts the "Social" in "Social Computing"?: Using A Neurodiversity Framing to Review Social Computing Researchcited by 2
  11. 2025The Social Construction of Visualizations: Practitioner Challenges and Experiences of Visualizing Race and Gender Demographic Datacited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.