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.