Whose Ground Truth? Position paper urges modeling human interpretation ambiguity in AI
This position paper (arXiv:2610.10805v1) argues that many human-centered tasks involve inherently ambiguous human interpretations and that treating annotation variability as noise or collapsing it to a single ground truth omits important information. The authors call for modeling the space of plausible human judgments and for changes to how AI systems are represented, learned, evaluated, deployed, and governed to reflect meaningful ambiguity.