arXiv subjects

Harper Strickland

Publications and source records attributed to Harper Strickland.

4 recordsLinked to original sources

arXiv2026

Algorithmic Fragility and Persona Bias in LLM-Generated Autistic Communication

Safety alignment reduces explicitly harmful outputs but inadvertently encodes a sanitized, neuronormative representation of marginalized communication. We investigate this encoding using a dual-persona rewrite paradigm, prompting ten large language models (LLMs) to rewrite naturally occurring autistic discourse from either an autistic or neurotypical persona. We uncover autistic-persona rewrites diverge significantly more in lexical form and affective register than neurotypical rewrites, despite equivalent semantic similarity. Furthermore, most models collapse cross-persona generations into near-identical outputs. To uncover the mechanisms behind this generative breakdown, we introduce a multi-agent qualitative analysis framework. Our results reveal systemic output erasure, stereotyped hallucination, and task-evasive meta-commentary are pervasive failure modes for this task that cluster by alignment strategy rather than parameter scale. Finally, our targeted comparison with autistic human annotators demonstrates that community-insider knowledge produces systematic label reversals relative to LLM classifications. Our findings indicate that current alignment training causes persona-specific generative breakdown visible only through qualitative analysis, confirming a deep representational gap that prompt engineering cannot resolve.

cs.CL↗

arXiv2025

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

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.

cs.CY↗

arXiv2025

Beyond Keywords: Evaluating Large Language Model Classification of Nuanced Ableism

Large language models (LLMs) are increasingly used in decision-making tasks like r\'esum\'e screening and content moderation, giving them the power to amplify or suppress certain perspectives. While previous research has identified disability-related biases in LLMs, little is known about how they conceptualize ableism or detect it in text. We evaluate the ability of four LLMs to identify nuanced ableism directed at autistic individuals. We examine the gap between their understanding of relevant terminology and their effectiveness in recognizing ableist content in context. Our results reveal that LLMs can identify autism-related language but often miss harmful or offensive connotations. Further, we conduct a qualitative comparison of human and LLM explanations. We find that LLMs tend to rely on surface-level keyword matching, leading to context misinterpretations, in contrast to human annotators who consider context, speaker identity, and potential impact. On the other hand, both LLMs and humans agree on the annotation scheme, suggesting that a binary classification is adequate for evaluating LLM performance, which is consistent with findings from prior studies involving human annotators.

cs.CL↗

arXiv2024

AUTALIC: A Dataset for Anti-AUTistic Ableist Language In Context

As our understanding of autism and ableism continues to increase, so does our understanding of ableist language towards autistic people. Such language poses a significant challenge in NLP research due to its subtle and context-dependent nature. Yet, detecting anti-autistic ableist language remains underexplored, with existing NLP tools often failing to capture its nuanced expressions. We present AUTALIC, the first benchmark dataset dedicated to the detection of anti-autistic ableist language in context, addressing a significant gap in the field. The dataset comprises 2,400 autism-related sentences collected from Reddit, accompanied by surrounding context, and is annotated by trained experts with backgrounds in neurodiversity. Our comprehensive evaluation reveals that current language models, including state-of-the-art LLMs, struggle to reliably identify anti-autistic ableism and align with human judgments, underscoring their limitations in this domain. We publicly release AUTALIC along with the individual annotations which serve as a valuable resource to researchers working on ableism, neurodiversity, and also studying disagreements in annotation tasks. This dataset serves as a crucial step towards developing more inclusive and context-aware NLP systems that better reflect diverse perspectives.

cs.CL↗