mlsanityData to 7 Oct 2026

Deceptive AI systems that give explanations are more convincing than honest AI systems and can amplify belief in misinformation

arXiv 2408.00024 ↗First posted 31 Jul 2024cs.AI cs.CYcited by 3

Not on the map. Why?

Abstract (arXiv)

Advanced Artificial Intelligence (AI) systems, specifically large language models (LLMs), have the capability to generate not just misinformation, but also deceptive explanations that can justify and propagate false information and erode trust in the truth. We examined the impact of deceptive AI generated explanations on individuals' beliefs in a pre-registered online experiment with 23,840 observations from 1,192 participants. We found that in addition to being more persuasive than accurate and honest explanations, AI-generated deceptive explanations can significantly amplify belief in false news headlines and undermine true ones as compared to AI systems that simply classify the headline incorrectly as being true/false. Moreover, our results show that personal factors such as cognitive reflection and trust in AI do not necessarily protect individuals from these effects caused by deceptive AI generated explanations. Instead, our results show that the logical validity of AI generated deceptive explanations, that is whether the explanation has a causal effect on the truthfulness of the AI's classification, plays a critical role in countering their persuasiveness - with logically invalid explanations being deemed less credible. This underscores the importance of teaching logical reasoning and critical thinking skills to identify logically invalid arguments, fostering greater resilience against advanced AI-driven misinformation.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 11 of its 52 references that are in the map. First posted on arXiv 31 Jul 2024.

Builds on

11 of 52 references.

References · 11

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023Jailbreaking ChatGPT via Prompt Engineering: An Empirical Studycited by 478
  3. 2018On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detectioncited by 159
  4. 2023Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigationscited by 157
  5. 2019"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanationscited by 111
  6. 2022Explanations Can Reduce Overreliance on AI Systems During Decision-Makingcited by 111
  7. 2023Art and the science of generative AI: A deeper divecited by 93
  8. 2021Deepfake Detection by Human Crowds, Machines, and Machine-informed Crowdscited by 58
  9. 2022Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learningcited by 53
  10. 2022Human Detection of Political Speech Deepfakes across Transcripts, Audio, and Videocited by 16
  11. 2021Do explanations increase the effectiveness of AI-crowd generated fake news warnings?cited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026Evaluating the False Trust Engendered by LLM Explanations
  2. 2026Think Before You Lie: How Reasoning Leads to Honesty
  3. 2025Simulating Misinformation Vulnerabilities With Agent Personas
  4. 2025When Thinking LLMs Lie: Unveiling the Strategic Deception in Representations of Reasoning Models
  5. 2025DeceptionBench: A Comprehensive Benchmark for AI Deception Behaviors in Real-world Scenarios
  6. 2025AI Debaters are More Persuasive when Arguing in Alignment with Their Own Beliefs
  7. 2024Deception and Manipulation in Generative AI
  8. 2025Evaluating the Simulation of Human Personality-Driven Susceptibility to Misinformation with LLMs