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

TL;DR

This paper explores a philosophical perspective in machine learning, proposing a framework that treats the true target as non-objectively existent, leading to new evaluation and learning methods under Democratic Supervision.

Contribution

It introduces a negative ontology approach to ML, defines Democratic Supervision, and develops the EL-MIATTs framework for evaluation and learning without assuming an objective true target.

Findings

Proposes the EL-MIATTs framework for ML evaluation and learning.

Demonstrates the framework's application in education and professional development.

Introduces the concept of Multiple Inaccurate True Targets (MIATTs).

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 world, and, grounded in this non-existence assumption, define Democratic Supervision for ML. We further present Multiple Inaccurate True Targets (MIATTs) as an instance-level realization of Democratic Supervision. Building upon MIATTs, we derive principles, for the logic-driven generation and assessment of MIATTs, a logical assessment formulation for evaluation with MIATTs, and undefinable true target learning for learning with MIATTs. Based on these components, we establish the evaluation and learning with MIATTs (EL-MIATTs) framework for ML-based predictive modelling. A real-world application demonstrates the potential of the proposed EL-MIATTs framework in supporting education and professional development for individuals, aligning with prior discussions of Democratic Supervision in the fields of education and professional development.