A Guide to Machine Learning Epistemic Ignorance, Hidden Paradoxes, and Other Tensions

Abstract

Machine learning (ML) has rapidly scaled in capacity and complexity, yet blind spots persist beneath its high performance façade. In order to shed more light on this argument, this paper presents a curated catalogue of 175 unconventional concepts, each capturing a paradox, tension, or overlooked risk in modern ML practice. Through nine themes spanning data quality, model architecture and training, interpretability and explainability, fairness and bias, model behavior and limitations, evaluation and metrics, multimodal and system integration, practical and societal implications, and causal reasoning, we provide conceptual definitions, illustrative examples, and actionable mitigation strategies. This review equips practitioners and researchers with a structured taxonomy for diagnosing and preempting the brittle edges of modern ML systems and offers a paradox detection and remediation framework (PDRF) to anticipate limitations, design more thoughtful evaluation protocols, and develop ML systems that balance predictive power with epistemic transparency.This article is categorized under: Fundamental Concepts of Data and Knowledge > Data Concepts Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Computational Intelligence