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
The proliferation of ML applications often reveals limitations in traditional evaluation, prompting philosophical re-examination of foundational assumptions.
This article suggests a fundamental re-evaluation of how AI models are designed, trained, and evaluated, moving towards a more human-centric and democratically supervised approach.
The proposed shift away from a 'true target' towards democratic supervision redefines success metrics and introduces new ethical and philosophical considerations for ML development.
- · Ethical AI researchers
- · Human-in-the-loop AI systems
- · AI governance frameworks
- · Purely objective ML evaluation paradigms
- · Black-box AI systems
Machine learning evaluation metrics become more aligned with human values and societal norms.
Development of new AI architectures and training methodologies that explicitly incorporate democratic feedback loops.
Increased public trust in AI systems due to transparent and human-aligned supervision mechanisms.
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Read at arXiv cs.LG