A decision architecture for epistemic prioritization: Machine learning at the intersection of technology and society

This review examines how machine learning (ML) methodologies are transforming the philosophy of science and engineering through five critical epistemic functions: Prediction, Explanation, Discovery, Understanding, and Decision-making (P.E.D.U.D.). We analyze each function individually and then provide examples of how ML applications embody these epistemic aims. Building on this analysis, we develop a framework to help users/practitioners determine which epistemic function to prioritize for specific problem domains by creating a decision architecture that aligns ML methodologies with epistemic goals. Finally, we explore the broader philosophical implications of this epistemological landscape by analyzing tensions between data-driven and theory-driven approaches and argue that ML necessitates a reconsideration of the traditional philosophy of science as the balance between these five functions evolves. • ML reshapes science via Prediction, Explanation, Discovery, Understanding & Decisions. • Develops a decision framework aligning ML methodologies with targeted epistemic goals. • Highlights tensions between data- and theory-driven science approaches in ML fueling!. • Examines ML's epistemic shift urging a reappraisal of science's classic frameworks … • Outlines challenges and future research directions at ML's epistemic nexus for change!!.