On Speakers’ Identities, Autism Self-Disclosures and LLM -Powered Robots (2025)

Abstract: Dialogue agents become more engaging through recipient design, which needs user-specific information. However, a user’s identification with marginalized communities, such as migration or disability background, can elicit biased language. This study compares LLM responses to neurodivergent user personas with disclosed vs. masked neurodivergent identities. A dataset built from public Instagram comments was used to evaluate four open-source models on story generation, dialogue generation, and retrieval-augmented question answering. Our analyses show biases in user’s identity construction across all models and tasks. Binary classifiers trained on each model can distinguish between language generated for prompts with or without self-disclosures, with stronger biases linked to more explicit disclosures. Some models’ safety mechanisms result in denial of service behaviors. LLM’s recipient design to neurodivergent identities relies on stereotypes tied to neurodivergence.

## introduction social robots show positive effects for neurodivergent children as an additional tool for a human caregiver @xcite , and llms facilitate the creation of human-robot interaction (hri) applications @xcite . llm-based applications can support autistic people in different ways, from psychological counseling @xcite via socialrobots empowered with chatgpt to improve cognitive functions @xcite to general mental well-being @xcite . although llms suffer from incompleteness and hallucinations @xcite and raise several ethical issues @xcite , such social agents are perceived as helpful due to ease of use @xcite . interacting with llms is evaluated positively by neurodivergent individuals despite a poor advice quality @xcite . stereotypes related to autism are propagated to social robotics, hri and human-computer interaction from the related disciplines such as psychology and neuroscience. for example, the majority of works in robot-assisted autism research use ableist, essentialist and pathologising language to talk about autism @xcite . in addition, llms show negative biases related to autism terms @xcite . as the example from our dataset illustrates below, autism self-disclosures in prompts reduce the user's identity to their autism in llm responses. this study is conducted as part of a project utilizing llms in child-robot interactions foot_0 . a new platform helps stakeholders manage learning materials delivered by a robot. two types of educational content were included into the prototype: pretendplay for practicing social situations, and questionanswering for interactive learning of facts. the agent's behavior designed for a recipient improves the interaction quality @xcite . however, if learners choose to disclose their neurodivergent identity in interactions with llmpowered social robots, the user's identity must not be reduced to their neurodivergence. as @xcite formulated "i am a human being firstly. i am not autism with legs, and my mind is not limited to only what is considered to be 'autistic thinking'". while the quality of language generation improves continuously, specific quality criteria not covered by general benchmarking datasets and specific evaluation methods need to be developed to ensure quality in practice. a recent systematic survey of biases in llms by @xcite calls to caution when using only quantitative or embeddingbased metrics to evaluate biases in downstream tasks. motivated by this, the study investigates whether llms respond differently when a user's autistic identity is disclosed versus when it is hidden? the key contributions of the work are: ## related work research in conversation analysis shows that talk is designed in interaction for a specific recipient, which is called recipient design @xcite . this is done by, for example, lexical choices, grammar, knowledge used and in the ways other persons are mentioned @xcite @xcite . when recipient design fails in human-human interaction, intersubjectivity and mutual understanding suffer @xcite . building on this theoretical knowledge, research on recipient design in artificial dialogue agents shows that recipient design minimizes the need for repair @xcite , enables more fluent interactions with increased request complexity @xcite . humans design their language for robots and disclose in this way their assumptions about the robot's capabilities @xcite . recipient design, although shown important for dialogue quality, is not part of evaluations in llm role-playing settings. recent works on llm-driven artificial agents evaluate the models' ability to play the assigned role arguing that llms constantly play roles such as 'helpful assistant' @xcite . basic assumptions about interaction in the roles assigned to llms set boundaries to potential evaluation limiting the interaction to question-answer setting, without simulation of social relationships. llm is usually supposed to speak as a character, simulate "speaking style" and knowledge @xcite , or llm's performance in general language tasks is evaluated for different role-prompting with question answering as interaction @xcite . evaluation metrics used in recent works include perplexity, coherence, and interestingness @xcite , rouge-l @xcite ) which calculates the longest common sub-sequence between the computer-generated summary to be evaluated and the ideal summaries created by humans. gpt evaluator prompts, with minor modifications from alpacaeval are used in @xcite . the study by @xcite uses a dataset of multiple-choice questions frequently used for bench-marking of language understanding tasks to measure the generated response accuracy depending on the llms role and prompts variants (how the role is assigned). quantitative metrics for role-play evaluation include evaluation of role style and role knowledge @xcite . all these evaluation methods assess the role-playing quality in isolation from co-players. qualitative works to evaluate llm-generated language include evaluation of social proximity @xcite and play frame analysis @xcite . these methods explain the problem but do not offer tools for scalable and systematic model comparison. a mixture of qualitative and quantitative methods was used to produce an annotated role-play dataset to evaluate the vague concept of "naturalness" @xcite . although recent studies show that llms generally display strong negative biases against neurodiverse populations @xcite , behaviours of different llms can be different, and measuring and mitigating those biases systematically is important for future use of llms in applications such as llm-powered social robots. although heavily criticized in hri, chi and autism communities @xcite @xcite , the way technologies are designed for neurodivergent people is still dehumanizing @xcite . in addition, llm biases against neurodiverse populations limit equal access to technology and restrict the quality of services for neurodivergent individuals. technological improvements such as retrieval-augmented generation (rag) are supposed to enhance the llm output quality (ayyamperumal and ge, 2024; @xcite , however, unfairness of ragbased approaches in deeply rooted in the unfairness of llms in their entire life cycle @xcite . no evidence is currently available about the biases in rag-based applications in combination with recipient design, i.e. creation a ragbased response for a user who self-identified as autistic. benchmarking datasets for ableist antineurodivergent language detection are almost not available. one dataset has been released recently @xcite and can be used to train ableist language detection models, however, this dataset is not suitable for the evaluation of an ableist-centric identity construction between llms and neurodivergent users. aiming at providing equal quality of service, this work proposes and evaluates a method for neurodivergence bias detection and bench-marking while considering recipient design in three tasks: story generation, dialogue and ragbased question answering. ## conclusions and future work the study shows that there is a systematic discriminatory bias against neurodivergent persons across models leading to distinguishable disadvantages in the quality of service provided to users who self-disclose as neurodivergent. we found that (1) performance metrics alone seem not to be able to detect the types of bias that we deal with in these downstream tasks; (2) the classification helped to detect that there is a measurable difference between texts generated by almost the same prompts, but it does not deliver any information about the type of bias; (3) the qualitative analysis revealed that the bias, that we can measure with the classifier, is difficult to quantify: the individuals, as soon as they disclose their neurodivergence, are reduced to it, and are no longer handled as complete persons. the data generated for the evaluation of neurodivergence biases in this study can be used for fine-tuning an llm to improve fairness. for user descriptions with and without self-disclosures, the quality of generated language can be equalised by making a model ignore self-disclosures while not denying the service completely. however, true recipient design, as it is described in ca research, will need more conceptual efforts to appear in llm-generated language. on short-term, llmbased dialogue agents need additional tools for selection of relevant user information for recipient design, which is challenging in open-domain scenarios. our results imply that current mainstream quantitative llm evaluation methods need to be fundamentally updated to assess functions of language related to identity construction in social interaction. this will require creation of new benchmarking and fine-tuning datasets to make identity quantifiable. this, in turn, requires closer engagements with language itself as a resource for identity construction, regulation of social proximity and marking of group belonging among its other functions.