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.

The trustworthiness of highly capable language models is put at risk when they are able to produce deceptive outputs. Moreover, when models are vulnerable to deception it undermines reliability. In this paper, we introduce a method to investigate complex, model-on-model deceptive scenarios. We create a dataset of over 10,000 misleading explanations by asking Llama-2 7B, 13B, 70B, and GPT-3.5 to justify the wrong answer for questions in the MMLU. We find that, when models read these explanations, they are all significantly deceived. Worryingly, models of all capabilities are successful at misleading others, while more capable models are only slightly better at resisting deception. We recommend the development of techniques to detect and defend against deception. Since the release of OpenAI's ChatGPT, large language models (LLMs) have revolutionized information accessibility by providing precise answers and supportive explanations to complex queries (Spatharioti et al., 2023; Caramancion, 2024; OpenAI, 2022). However, LLMs have also demonstrated a propensity to hallucinate explanations that are convincing but incorrect (Zhang et al., 2023; Walters & Wilder, 2023; Xu et al., 2024).

Artificial intelligence comes with great opportunities and but also great risks. We investigate to what extent deep learning can be used to create and detect deceptive explanations that either aim to lure a human into believing a decision that is not truthful to the model or provide reasoning that is non-faithful to the decision. Our theoretical insights show some limits of deception and detection in the absence of domain knowledge. For empirical evaluation, we focus on text classification. To create deceptive explanations, we alter explanations originating from GradCAM, a state-of-art technique for creating explanations in neural networks. We evaluate the effectiveness of deceptive explanations on 200 participants. Our findings indicate that deceptive explanations can indeed fool humans. Our classifier can detect even seemingly minor attempts of deception with accuracy that exceeds 80% given sufficient domain knowledge encoded in the form of training data. 1 Introduction Because of the limited moderation of online content, attempts at deception proliferate. Online media struggle against the plague of "fake news", and e-commerce sites spend considerable effort in detecting deceptive product reviews.