The main way large language models (LLMs) learn to represent and interpret various experiences is through the process of supervised fine-tuning (SFT). However, current practices are not designed to be inclusive for people with ADHD, which leads to generative hermeneutical ignorance due to misrepresentation. Several ADHD characteristics clash with modern annotation task structures, so those voices remain underrepresented. We performed a literature-driven gap analysis, derived five design requirements and evaluation criteria and built an annotation interface that embodied those requirements. Consequently, a mixed approach user study with seven self-identified ADHD participants was conducted to measure behavioral metrics and collect post-task reflections. The results indicated that three of five design criteria were met, which is promising. However, the average mislabeling rate remained quite high, meaning that accuracy is still an open issue. Finally, our study demonstrated that small design adjustments accommodate a more diverse annotator pool, thus, we offer a framework that can be used to reinforce hermeneutical epistemic justice in annotation practices.

Generative AI can contribute to the misunderstanding or erasure of marginalized groups due to the insufficient nuanced data on their lived experiences. This limits the shared un- derstanding of their perspectives and contributes to a phenomenon called hermeneutical epistemic injustice. This study seeks to reduce this injustice by enabling real-life users from these groups to provide feedback that corrects the behavior of the model. However, victims of hermeneutical injustice struggle with articulating themselves, and current prac- tices lack sufficient support for user expression. Overcoming these challenges, we designed an interface to enable users to give feedback on the accuracy of the model, supported by a data processing workflow to ensure feasibility and scalability. We conducted a user study with 8 individuals with ADHD to evaluate whether the interface facilitates the extraction of accurate data, and found that it enables users to provide more concrete and precise feedback than existing methods, as it includes more guidance and control for the user.

This study investigates how large language models (LLMs) narrate ADHD-related experiences and whether their narrative forms give rise to hermeneutical injustice. Rather than comparing experience itself, this study analyzes how experiences are narrated. Using a hybrid coding strategy based on Reflexive Thematic Analysis, it compares LLM-generated outputs with first-person narratives from ADHD communities. The analysis identifies several recurring misnarration patterns, Truncated Subjectivity, One-Way Definition, Illocutionary Disablement, and Skewed Style Replacement. Each of these patterns constrains the interpretive space for expressing ADHD experience. Sub-themes are developed to further reveal injustice embedded in LLMs. These patterns are linked to both the training data and the optimization process. In addition, the underlying mechanism of LLMs lacks the différance structure that characterizes human narration.

This study investigates how hermeneutical injustices can become encoded in the Reinforcement Learning from Human Feedback processes used to fine-tune large language models (LLMs). While current research on fairness in LLMs has focused on bias and fairness, there remains a significant gap concerning subtler harms such as hermeneutical injustice. Using adults diagnosed with ADHD as a case study, this research explores how their unique communication and cognitive patterns may be misrepresented or excluded from the RLHF pipeline. The research adopts a qualitative literature review methodology, focusing specifically on real-world RLHF implementations by AI companies. The RLHF pipeline was divided into stages of human feedback collection, reward modeling, and policy optimization. Then, these stages of the RLHF were analyzed through the lens of hermeneutical injustice using interpretive desiderata: representation, flexibility, and authenticity. The findings highlight several conceptual risks. Limited annotator diversity and restrictive feedback formats may exclude neurodivergent voices. Reward models can unintentionally suppress atypical expressions, while policy optimization strategies, especially those prone to mode collapse, can erase some communication styles. Overall, the study shows that without deliberate attention to epistemic inclusion, RLHF processes may perpetuate hermeneutical injustices and undermining the epistemic fairness of LLMs.

Large Language Models are increasingly integrated into everyday applications, but their responses often reflect dominant cultural narratives, which can lead to misrepresentation of marginalized communities. This paper addresses the underexplored issue of hermeneutical epistemic injustice (HEI) in LLM outputs, particularly how these systems fail to accurately represent the lived experiences of people with ADHD when answering causal questions, and whether different prompting techniques can influence and improve the justice reflected in their responses. We introduce a practical framework for measuring HEI based on four proxies: intelligibility, conceptual fit, recognition of structural barriers, and expression style. Through a within-subjects user study with seven adults with ADHD, we evaluated three prompting strategies: Vanilla (baseline), Step-Back, and Human Persona + System 2. Our findings show that Human Persona + System 2 prompting stood out for its empathetic tone, balanced perspectives, and non-judgmental framing, thereby improving fairness across multiple HEI dimensions. Surprisingly, Vanilla prompts performed comparably well overall, while Step-Back responses offered clear practical information and contextual relevance, but were limited by an impassive, matter-of-fact tone. These results suggest that prompt design can meaningfully affect how well LLMs represent marginalized experiences. We conclude that advancing epistemic justice in generative AI requires thoughtful prompt design and may benefit from deeper engagement with affected communities to more accurately and respectfully represent their realities.