Network structure affects how AI fights deception with truth or checks
Network Topology Shapes AI Intervention Effectiveness on Deception: Truth-Telling vs. Investigative Agents
Computer Science and Game Theory
Summary
People and AI sometimes communicate dishonestly, and AI agents can help catch or counter lies. This study looks at how AI agents set to always tell the truth or to actively investigate others influence honesty in social networks. The authors found that simply adding truth-telling AI can sometimes make honest people easier to deceive, while investigative AI encourages more truthful behavior. The impact depends on how many AI are involved and the kind of network connections between people and AI.
What this means in practice
- •For social network platform teams: Optimize placement and type of AI moderators to reduce deceptive content considering user connection patterns.
- •For cybersecurity analysts: Design AI watchdog agents for communication networks that actively investigate suspicious behavior to promote truthful exchanges.
Tested on simulated data.
Authors
Zhao Song, Stefan Sarkadi, The Anh Han
Abstract
Deploying AI agents to combat dishonest communication is an intuitive response to deception in hybrid human-AI societies. However, the efficacy of such interventions in realistic networked settings remains unclear. We study this problem using an evolutionary game-theoretic model of the Deceiver's Game by extending it to structured networks, where agents can adopt deception, truth-telling, and investigation strategies. We introduce committed AI agents (zealots) fixed to either truth-telling or investigative strategies and conduct agent-based simulations across three network topologies. Our key findings are: (1) passive committed truth-telling agents can amplify adaptive deception by creating exploitable honest partners, whereas investigative agents increase adaptive truth-telling in the oscillatory regime; (2) intervention effects are non-monotonic in deployment density because committed agents reshape rather than simply suppress the underlying oscillatory dynamics; and (3) response peaks depend on topology, with sampled scale-free populations showing sharp low-density responses and fully connected populations requiring larger committed fractions. Overall, the results highlight the need to distinguish passive truth-telling from active investigation when using committed agents to influence networked deception dynamics.