Unexplainability of Artificial Intelligence Judgments in Kant's Perspective

TL;DR

This paper explores the limitations of AI judgment explainability through Kant's epistemology, highlighting how AI's inherent uncertainty and linguistic unexplainability challenge its ability to replicate human judgment.

Contribution

It applies Kant's logical framework to analyze AI judgments, revealing fundamental unexplainability issues rooted in AI's structure and natural language limitations.

Findings

AI judgments exhibit entangled forms of uncertainty.

SoftMax function reframes AI judgments as possibility judgments.

Complete natural language definitions are inherently unexplainable.

Abstract

Kant's Critique of Pure Reason, a major contribution to the history of epistemology, proposes a table of categories to elucidate the structure of the a priori principles underlying human judgment. Artificial intelligence (AI) technology, grounded in functionalism, claims to simulate or replicate human judgment. To evaluate this claim, it is necessary to examine whether AI judgments exhibit the essential characteristics of human judgment. This paper investigates the unexplainability of AI judgments through the lens of Kant's theory of judgment. Drawing on Kant's four logical forms-quantity, quality, relation, and modality-this study identifies what may be called AI's uncertainty, a condition in which different forms of judgment become entangled. In particular, with regard to modality, this study argues that the SoftMax function forcibly reframes AI judgments as possibility judgments. Furthermore, since complete definitions in natural language are impossible, words are, by their very nature, ultimately unexplainable; therefore, a fully complete functional implementation is theoretically unattainable.