Negative Ontology of True Target for Machine Learning: Towards Evaluation and Learning under Democratic Supervision

arXiv:2604.24824v3 Announce Type: replace Abstract: This article philosophically examines how shifts in assumptions regarding the existence and non-existence of the true target (TT) give rise to new perspectives and insights for machine learning (ML)-based predictive modeling and, correspondingly, proposes a knowledge system for evaluation and learning under Democratic Supervision. By systematically analysing the existence assumption of the TT in current mainstream ML paradigms, we explicitly adopt a negative ontology perspective, positing that the TT does not objectively exist in the real wor

Source: arXiv cs.LG — read the full report at the original publisher.

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