Summary of Unexplainability Of Artificial Intelligence Judgments in Kant’s Perspective, by Jongwoo Seo
Unexplainability of Artificial Intelligence Judgments in Kant’s Perspective
by Jongwoo Seo
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel investigation into the realm of artificial intelligence (AI) challenges the notion that machine learning systems can replicate human judgment by examining the underlying structures and characteristics of both AI and human decision-making processes. The study draws parallels between Kant’s Critique of Pure Reason, which posits a table of categories to elucidate the structure of human judgment, and AI’s functionalist approach to simulating human-like reasoning. The researchers argue that AI judgments exhibit a distinct form that cannot be understood in terms of traditional human judgment characteristics, leading to the concept of “uncertainty” in AI decision-making. Furthermore, they demonstrate that concepts without physical intuitions are challenging to explain through vision-based functions and that even when AI generates sentences through subject-predicate structures in natural language, it is difficult to determine if AI truly comprehends these concepts at a level acceptable to humans. This inquiry raises questions about the reliability of explanations provided by AI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI tries to make decisions like humans do, but can machines really think like us? Researchers studied how artificial intelligence (AI) makes judgments and found that it works differently than human judgment. They compared AI’s approach to a famous book on human thinking from 1781. The scientists discovered that AI judgments have their own special way of working, which they called “uncertainty”. They also showed that when AI tries to explain things using words and sentences like humans do, it can be tricky to figure out if the machine really understands what it’s saying. |
Keywords
» Artificial intelligence » Machine learning
