Epistemic Trade-Off: An Analysis of the Operational Breakdown and Ontological Limits of "Certainty-Scope" in AI

TL;DR

This paper critically examines the 'certainty-scope' conjecture in AI, highlighting its philosophical significance but also its practical limitations due to incomputable constructs and ontological assumptions, which hinder its application in real-world AI systems.

Contribution

It identifies fundamental theoretical and ontological limitations of the certainty-scope conjecture, proposing a reframing of AI epistemic challenges in complex socio-technical contexts.

Findings

The conjecture relies on incomputable constructs, limiting practical implementation.

It assumes AI systems as self-contained epistemic entities, ignoring socio-technical dynamics.

These limitations prevent the conjecture from guiding real-world AI deployment effectively.

Abstract

The recently published "certainty-scope" conjecture offers a compelling insight into the inherent trade-off present within artificial intelligence (AI) systems. As general research, this investigation remains vital as a philosophical undertaking and a potential guide for directing AI investments, design, and deployment, especially in safety-critical and mission-critical domains where risk levels are substantially elevated. While maintaining intellectual coherence, its formalization ultimately consolidates this insight into a suspended epistemic truth, which resists operational implementation within practical systems. This paper argues that the conjecture's objective to furnish insights for engineering design and regulatory decision-making is limited by two fundamental factors: first, its dependence on incomputable constructs and its failure to capture the generality factors of AI, rendering it practically unimplementable and unverifiable; second, its foundational ontological assumption of AI systems as self-contained epistemic entities, distancing it from the complex and dynamic socio-technical environments where knowledge is co-constructed. We conclude that this dual breakdown - an epistemic closure deficit and an embeddedness bypass - hinders the conjecture's transition to a practical and actionable framework suitable for informing and guiding AI deployments. In response, we point towards a possible framing of the epistemic challenge, emphasizing the inherent epistemic burdens of AI within complex human-centric domains.