Evaluability as a Precondition for Ethical Reasoning in Artificial Intelligence

Contemporary AI ethics has developed a robust normative framework emphasizing fairness, accountability, transparency, explainability, governance, and meaningful human oversight. These principles seek to ensure that increasingly capable AI systems remain subject to responsible human judgment. This paper argues, however, that they implicitly depend upon an underlying informational condition that has received comparatively little explicit attention. It identifies this condition as evaluability, defined as the continuing practical capacity to independently inspect, reconstruct, compare, challenge, and assess the informational foundations of claims and decisions.

As AI increasingly mediates the creation, transformation, retrieval, synthesis, and circulation of information, the practical conditions supporting independent ethical reasoning may progressively weaken even while ethical expectations of human oversight continue to expand. The paper develops this tension as an emerging ethical paradox and argues that preserving evaluability should itself be understood as an ethical obligation. It further reconceptualizes verification as preservation infrastructure that preserves the informational continuity necessary for future independent ethical reasoning. Rather than proposing an alternative framework for AI ethics, the paper identifies evaluability as a foundational condition upon which many existing ethical commitments already depend.