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

• URL: http://arxiv.org/abs/2508.19304v1
• Date: Tue, 26 Aug 2025 05:47:21 GMT
• Title: Epistemic Trade-Off: An Analysis of the Operational Breakdown and Ontological Limits of "Certainty-Scope" in AI
• Authors: Generoso Immediato,
• Abstract summary: Floridi's conjecture offers a compelling intuition about the fundamental trade-off between certainty and scope in AI systems.<n>This paper argues that the conjecture's ambition to provide insights to engineering design and regulatory decision-making is constrained by two critical factors.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Floridi's conjecture offers a compelling intuition about the fundamental trade-off between certainty and scope in artificial intelligence (AI) systems. This exploration remains crucial, not merely as a philosophical exercise, but as a potential compass for guiding AI investments, particularly in safety-critical industrial domains where the level of attention will surely be higher in the future. However, while intellectually coherent, its formalization ultimately freezes this insight into a suspended epistemic truth, resisting operationalization within real-world systems. This paper is a result of an analysis arguing that the conjecture's ambition to provide insights to engineering design and regulatory decision-making is constrained by two critical factors: first, its reliance on incomputable constructs - rendering it practically unactionable and unverifiable; second, its underlying ontological assumption of AI systems as self-contained epistemic entities - separating it from the intricate and dynamic socio-technical environments in which knowledge is co-constructed. We conclude that this dual breakdown - an epistemic closure deficit and an embeddedness bypass - prevents the conjecture from transitioning into a computable and actionable framework suitable for informing the design, deployment, and governance of real-world AI hybrid systems. In response, we propose a contribution to the framing of Floridi's epistemic challenge, addressing the inherent epistemic burdens of AI within complex human-centric domains.

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