This article philosophically examines how a shift in the assumed ontology of the true target (TT) can lead to a new paradigm for machine learning (ML)-based predictive modelling. By systematically analysing the existence assumption of the TT underlying mainstream ML paradigms, we adopt a negative ontology perspective, explicitly positing that the TT does not objectively exist in the real world. On this basis, we define Democratic Supervision as an alternative supervisory principle for ML, in which no single source is assumed to possess an objectively privileged target. We further introduce Multiple Inaccurate True Targets (MIATTs) as an instance-level realization of Democratic Supervision. Building upon MIATTs, we establish the logic-driven generation and assessment for MIATTs construction (recognition with MIATTs), formulate logical assessment formula for evaluation with MIATTs, and develop undefinable true target learning for learning with MIATTs. These components are integrated to formulate the Recognition, Evaluation, Learning with MIATTs (REL-MIATTs) framework. We further characterize REL-MIATTs from the perspective of Cognitive Machine Learning: a single cycle provides an elementary cognitive learning unit, MIATTs introduce complementary supervisory perspectives, and iterative cycles support the evolution of learning through accumulated experience, feedback, and state updating. Extending this process to human-AI interaction provides a basis for human-AI co-cognitive development and, potentially, human-AI co-evolution. A real-world application further demonstrates the potential of the framework for supporting individual education and professional development, providing empirical evidence for the feasibility of Democratic Supervision, REL-MIATTs, and its broader implications for continuous human-AI co-evolution.

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