A new paper critiques the concept of "ground truth" in data annotation for machine learning, arguing that human disagreement is often treated as noise rather than a valuable signal. The research highlights how factors like positional legibility, reliance on model-mediated annotations, and geographic hegemony contribute to a "consensus trap." The authors propose a shift from seeking a single correct answer to mapping the diversity of human experience for more culturally competent AI models. AI
影响 Challenges the notion of "ground truth" in AI training data, potentially impacting how future models are evaluated and developed for cultural competence.
排序理由 The cluster contains an academic paper published on arXiv.
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