A perceptual bias of AI Logical Argumentation Ability in Writing

• URL: http://arxiv.org/abs/2511.22151v1
• Date: Thu, 27 Nov 2025 06:39:11 GMT
• Title: A perceptual bias of AI Logical Argumentation Ability in Writing
• Authors: Xi Cun, Jifan Ren, Asha Huang, Siyu Li, Ruzhen Song,
• Abstract summary: The ability of logical reasoning like humans is often used as a criterion to assess whether a machine can think.<n>This study explores whether human biases influence evaluations of the reasoning abilities of AI.
• Score: 3.1238547837436115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Can machines think? This is a central question in artificial intelligence research. However, there is a substantial divergence of views on the answer to this question. Why do people have such significant differences of opinion, even when they are observing the same real world performance of artificial intelligence? The ability of logical reasoning like humans is often used as a criterion to assess whether a machine can think. This study explores whether human biases influence evaluations of the reasoning abilities of AI. An experiment was conducted where participants assessed two texts on the same topic, one AI generated and one human written,to test for perceptual biases in evaluating logical reasoning. Based on the experimental findings, a questionnaire was designed to quantify the attitudes toward AI.The results reveal a bias in perception. The evaluations of the logical reasoning ability of AI generated texts are significantly influenced by the preconceived views on the logical reasoning abilities of AI. Furthermore, frequent AI users were less likely to believe that AI usage undermines independent thinking.This study highlights the need to address perceptual biases to improve public understanding of AI's capabilities and foster better human AI interactions.

Related papers

• 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.<n>Most AI systems offer "unilateral" explanations that justify the AI's decision but do not account for users' thinking.<n>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: Examining Human Judgment Against Text Labeled as AI Generated [48.70176791365903]
  This study explores how bias shapes the perception of AI versus human generated content.<n>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)
• Deceptive AI systems that give explanations are more convincing than honest AI systems and can amplify belief in misinformation [29.022316418575866]
  We examined the impact of deceptive AI generated explanations on individuals' beliefs. Our results show that personal factors such as cognitive reflection and trust in AI do not necessarily protect individuals from these effects. This underscores the importance of teaching logical reasoning and critical thinking skills to identify logically invalid arguments.
  arXiv  Detail & Related papers  (2024-07-31T05:39:07Z)
• Navigating AI Fallibility: Examining People's Reactions and Perceptions of AI after Encountering Personality Misrepresentations [7.256711790264119]
  Hyper-personalized AI systems profile people's characteristics to provide personalized recommendations. These systems are not immune to errors when making inferences about people's most personal traits. We present two studies to examine how people react and perceive AI after encountering personality misrepresentations.
  arXiv  Detail & Related papers  (2024-05-25T21:27:15Z)
• Can Machines Imitate Humans? Integrative Turing-like tests for Language and Vision Demonstrate a Narrowing Gap [56.611702960809644]
  We benchmark AI's ability to imitate humans in three language tasks and three vision tasks.<n>Next, we conducted 72,191 Turing-like tests with 1,916 human judges and 10 AI judges.<n>Imitation ability showed minimal correlation with conventional AI performance metrics.
  arXiv  Detail & Related papers  (2022-11-23T16:16:52Z)
• On the Influence of Explainable AI on Automation Bias [0.0]
  We aim to shed light on the potential to influence automation bias by explainable AI (XAI) We conduct an online experiment with regard to hotel review classifications and discuss first results.
  arXiv  Detail & Related papers  (2022-04-19T12:54:23Z)
• 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)
• 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)
• Does Explainable Artificial Intelligence Improve Human Decision-Making? [17.18994675838646]
  We compare and evaluate objective human decision accuracy without AI (control), with an AI prediction (no explanation) and AI prediction with explanation. We find any kind of AI prediction tends to improve user decision accuracy, but no conclusive evidence that explainable AI has a meaningful impact. Our results indicate that, at least in some situations, the "why" information provided in explainable AI may not enhance user decision-making.
  arXiv  Detail & Related papers  (2020-06-19T15:46:13Z)

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.