AI researchers question objectivity of ground truth datasets

This position paper argues that ground truth datasets, crucial for training and evaluating machine learning models, are not objective measurements but rather human constructions. The authors contend that the machine learning community would benefit from acknowledging the contingent and context-dependent nature of these datasets. By focusing on the situated reliability of ground truths, researchers can gain a better understanding of a model's limitations and strengths, thereby improving transparency and accountability. AI

IMPACT Prompts a deeper understanding of data limitations, potentially leading to more robust and reliable AI models.

RANK_REASON The item is an academic paper published on arXiv discussing theoretical aspects of machine learning datasets. [lever_c_demoted from research: ic=1 ai=1.0]

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