Unexplainability of Artificial Intelligence Judgments in Kant's Perspective (2407.18950v4)

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. 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.

• The paper critically compares AI judgments with Kant's four categories, highlighting AI's inability to mimic human cognitive structures.
• It employs a Kantian framework to analyze gaps in quantity, quality, relation, and modality within AI decision-making.
• The study underscores risks of overinterpreting AI outputs and calls for cautious advancement in explainable AI research.

Exploring the Unexplainability of AI Judgments through Kant's Epistemological Lens

The paper entitled "Unexplainability of Artificial Intelligence Judgments in Kant's Perspective" authored by Jongwoo Seo explores an intricate examination of the fundamental nature of AI judgments through the philosophical insights provided by Immanuel Kant. The author seeks to scrutinize the alignment, or lack thereof, of AI's decision-making processes with Kantian theories of human judgment, illuminating the limitations of current AI systems in mimicking human-like cognitive abilities.

Kantian Framework and AI Judgments

Kant's critical philosophy offers a foundational understanding of human judgment characterized by four categories: Quantity, Quality, Relation, and Modality. These categories provide a structural framework for assessing whether AI operates with a similar cognitive fidelity as humans do. Seo argues that AI's judgments are deeply entrenched in a kind of "unexplainability," wherein its capacity to exhibit human-like judgment is uncertain and fundamentally different from human cognition. Here, the paper highlights the notion of "AI's uncertainty," drawing a parallel to the uncertainty principle in quantum mechanics, to denote the ambiguous overlap of judgment characteristics in AI.

Unexplainability Across Kant's Categories

The paper meticulously explores how AI's judgments fall short across all four of Kant's categories.

1. Quantity: Unlike human cognition, AI's numerical outputs in tasks such as image classification do not correlate directly with Kant's logical form of judgments—Universal, Particular, Singular.
2. Quality: The distinction between Affirmative, Negative, and Infinite judgments is challenging to ascribe to AI outputs due to their mathematical, rather than conceptual, nature.
3. Relation: AI's inability to express explicit causal relationships within its outputs underscores its challenges with the category of Relation, which includes Categorical, Hypothetical, and Disjunctive judgments.
4. Modality: Although advancements (e.g., the use of the SoftMax function) partially aid AI in exhibiting Problematic judgments, other forms such as Assertorial or Apodeictical remain beyond AI's grasp.

Given these discrepancies, the author brings attention to the risk of misinterpreting AI's task-specific outputs as reflective of true understanding or reasoning akin to human thought processes.

Implications for Explainable AI

Seo interrogates the potential and limitations of explainable AI technologies. While techniques like Grad-CAM provide valuable insights by highlighting areas of interest in vision tasks, they do not guarantee an understanding of the underlying concepts. The emphasis on visual output analysis overlooks Kant's assertion regarding certain concepts—like self-consciousness—that lack physical intuition and, therefore, resist visual representation.

Furthermore, reliance on NLP for explicating AI judgments is scrutinized. Although NLP can output sentences resembling human language, trust in these outputs assumes a level of conceptual understanding by AI that remains unproven and questionable.

Theoretical and Practical Implications

This research presents significant theoretical implications, highlighting the discord between human and machine cognition through a Kantian lens. Practically, it calls into question the reliability of AI systems in applications requiring nuanced judgment and understanding inherently human concepts. The paper cautions against overly optimistic interpretations of AI outputs and underscores a need for cautious advancement in the development of AI systems that claim to replicate human intelligence.

Future Developments

Speculation about the future of AI, as guided by the insights from this article, suggests a research trajectory focused on narrowing the gap between human cognitive abilities and AI's computational capabilities. This might involve not only advancing explainable AI techniques but also fostering interdisciplinary dialogues among AI researchers, philosophers, and cognitive scientists. The exploration of AI under a Kantian framework provides a valuable perspective that could enrich discussions on the role of AI in society and its alignment with human cognitive processes.

Overall, this paper by Jongwoo Seo challenges existing paradigms in AI research by framing AI judgments within a philosophical context that questions the very nature of understanding and reliability, posing fundamental questions germane to the future of AI development and its ethical implications.