Unexplainability of Artificial Intelligence Judgments in Kant's Perspective

• URL: http://arxiv.org/abs/2407.18950v2
• Date: Sun, 8 Sep 2024 14:33:08 GMT
• Title: Unexplainability of Artificial Intelligence Judgments in Kant's Perspective
• Authors: Jongwoo Seo,
• Abstract summary: This paper argues that AI judgments exhibit a form that cannot be understood in terms of the characteristics of human judgments according to Kant. I show that concepts without physical predicates are not easy to explain when their functions are shown through vision.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 principle of human judgment. The technology of artificial intelligence (AI), based on functionalism, claims to simulate or replicate human judgment. To assess this claim, it is necessary to study whether AI judgment possesses the characteristics of human judgment. This paper argues that AI judgments exhibit a form that cannot be understood in terms of the characteristics of human judgments according to Kant. Because the characteristics of judgment overlap, we can call this AI's uncertainty. Then, I show that concepts without physical intuitions are not easy to explain when their functions are shown through vision. Finally, I illustrate that even if AI makes sentences through subject and predicate in natural language, which are components of judgment, it is difficult to determine whether AI understands the concepts to the level humans can accept. This shows that it is questionable whether the explanation through natural language is reliable.

Related papers

• A.I. go by many names: towards a sociotechnical definition of artificial intelligence [0.0]
  Defining artificial intelligence (AI) is a persistent challenge, often muddied by technical ambiguity and varying interpretations. This essay makes a case for a sociotechnical definition of AI, which is essential for researchers who require clarity in their work.
  arXiv  Detail & Related papers  (2024-10-17T11:25:50Z)
• Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skills [24.04643864795939]
  People's decision-making abilities often fail to improve when they rely on AI for decision-support. Most AI systems offer "unilateral" explanations that justify the AI's decision but do not account for users' thinking. We introduce a framework for generating human-centered contrastive explanations that explain the difference between AI's choice and a predicted, likely human choice.
  arXiv  Detail & Related papers  (2024-10-05T18:21:04Z)
• Human Bias in the Face of AI: The Role of Human Judgement in AI Generated Text Evaluation [48.70176791365903]
  This study explores how bias shapes the perception of AI versus human generated content. We investigated how human raters respond to labeled and unlabeled content.
  arXiv  Detail & Related papers  (2024-09-29T04:31:45Z)
• On the consistent reasoning paradox of intelligence and optimal trust in AI: The power of 'I don't know' [79.69412622010249]
  Consistent reasoning, which lies at the core of human intelligence, is the ability to handle tasks that are equivalent. CRP asserts that consistent reasoning implies fallibility -- in particular, human-like intelligence in AI necessarily comes with human-like fallibility.
  arXiv  Detail & Related papers  (2024-08-05T10:06:53Z)
• Alterfactual Explanations -- The Relevance of Irrelevance for Explaining AI Systems [0.9542023122304099]
  We argue that in order to fully understand a decision, not only knowledge about relevant features is needed, but that the awareness of irrelevant information also highly contributes to the creation of a user's mental model of an AI system. Our approach, which we call Alterfactual Explanations, is based on showing an alternative reality where irrelevant features of an AI's input are altered. We show that alterfactual explanations are suited to convey an understanding of different aspects of the AI's reasoning than established counterfactual explanation methods.
  arXiv  Detail & Related papers  (2022-07-19T16:20:37Z)
• Diagnosing AI Explanation Methods with Folk Concepts of Behavior [70.10183435379162]
  We consider "success" to depend not only on what information the explanation contains, but also on what information the human explainee understands from it. We use folk concepts of behavior as a framework of social attribution by the human explainee.
  arXiv  Detail & Related papers  (2022-01-27T00:19:41Z)
• Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
  Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
  arXiv  Detail & Related papers  (2022-01-26T18:53:09Z)
• Some Critical and Ethical Perspectives on the Empirical Turn of AI Interpretability [0.0]
  We consider two issues currently faced by Artificial Intelligence development: the lack of ethics and interpretability of AI decisions. We experimentally show that the empirical and liberal turn of the production of explanations tends to select AI explanations with a low denunciatory power. We propose two scenarios for the future development of ethical AI: more external regulation or more liberalization of AI explanations.
  arXiv  Detail & Related papers  (2021-09-20T14:41:50Z)
• The Who in XAI: How AI Background Shapes Perceptions of AI Explanations [61.49776160925216]
  We conduct a mixed-methods study of how two different groups--people with and without AI background--perceive different types of AI explanations. We find that (1) both groups showed unwarranted faith in numbers for different reasons and (2) each group found value in different explanations beyond their intended design.
  arXiv  Detail & Related papers  (2021-07-28T17:32:04Z)
• Inductive Biases for Deep Learning of Higher-Level Cognition [108.89281493851358]
  A fascinating hypothesis is that human and animal intelligence could be explained by a few principles. This work considers a larger list, focusing on those which concern mostly higher-level and sequential conscious processing. The objective of clarifying these particular principles is that they could potentially help us build AI systems benefiting from humans' abilities.
  arXiv  Detail & Related papers  (2020-11-30T18:29:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.