# Deception Analysis with Artificial Intelligence: An Interdisciplinary Perspective

arXiv: https://arxiv.org/abs/2406.05724 · HTML: https://mlsanity.com/paper/2406.05724/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 133 references that are in the map.
- First posted on arXiv 9 Jun 2024.

## Builds on (most cited first, 7 of 7)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems](https://mlsanity.com/paper/1709.08071/) (2017)
- [Learning to Deceive in Multi-Agent Hidden Role Games](https://mlsanity.com/paper/2209.01551/) (2022)
- [The politics of deceptive borders: 'biomarkers of deceit' and the case of iBorderCtrl](https://mlsanity.com/paper/1911.09156/) (2019)
- [Towards the Role of Theory of Mind in Explanation](https://mlsanity.com/paper/2005.02963/) (2020)
- [Modelling Strategic Deceptive Planning in Adversarial Multi-Agent Systems](https://mlsanity.com/paper/2109.03092/) (2021)
- [Dynamics of deceptive interactions in social networks](https://mlsanity.com/paper/1509.03918/) (2015)

## Built on by (most cited first, 4 of 4)

- [Towards Safe and Honest AI Agents with Neural Self-Other Overlap](https://mlsanity.com/paper/2412.16325/) (2024)
- [Information-theoretic Distinctions Between Deception and Confusion](https://mlsanity.com/paper/2501.16448/) (2025)
- [OntoPret: An Ontology for the Interpretation of Human Behavior](https://mlsanity.com/paper/2510.23553/) (2025)
- [DeceptionAnalyser: A Web-Based AI Tool for Performing Structured Deception Analysis with Argumentation Schemes and LLMs](https://mlsanity.com/paper/2609.24369/) (2026)

## Abstract (arXiv)

Humans and machines interact more frequently than ever and our societies are becoming increasingly hybrid. A consequence of this hybridisation is the degradation of societal trust due to the prevalence of AI-enabled deception. Yet, despite our understanding of the role of trust in AI in the recent years, we still do not have a computational theory to be able to fully understand and explain the role deception plays in this context. This is a problem because while our ability to explain deception in hybrid societies is delayed, the design of AI agents may keep advancing towards fully autonomous deceptive machines, which would pose new challenges to dealing with deception. In this paper we build a timely and meaningful interdisciplinary perspective on deceptive AI and reinforce a 20 year old socio-cognitive perspective on trust and deception, by proposing the development of DAMAS -- a holistic Multi-Agent Systems (MAS) framework for the socio-cognitive modelling and analysis of deception. In a nutshell this paper covers the topic of modelling and explaining deception using AI approaches from the perspectives of Computer Science, Philosophy, Psychology, Ethics, and Intelligence Analysis.