Trust as Monitoring: Evolutionary Dynamics of User Trust and AI Developer Behaviour
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arXiv
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| Natura: | Preprint |
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2026
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| author | Bashir, Adeela Song, Zhao Ogbo, Ndidi Bianca Balabanova, Nataliya Smit, Martin Leung, Chin-wing Bova, Paolo Serrano, Manuel Chica Dissanayake, Dhanushka Duong, Manh Hong Domingos, Elias Fernandez Huber-Kralj, Nikita Krellner, Marcus Powell, Andrew Sarkadi, Stefan Santos, Fernando P. Shamszaman, Zia Ush Tarzi, Chaimaa Turrini, Paolo Ufeoshi, Grace Ibukunoluwa Vargas-Perez, Victor A. Di Stefano, Alessandro Powers, Simon T. Han, The Anh |
| author_facet | Bashir, Adeela Song, Zhao Ogbo, Ndidi Bianca Balabanova, Nataliya Smit, Martin Leung, Chin-wing Bova, Paolo Serrano, Manuel Chica Dissanayake, Dhanushka Duong, Manh Hong Domingos, Elias Fernandez Huber-Kralj, Nikita Krellner, Marcus Powell, Andrew Sarkadi, Stefan Santos, Fernando P. Shamszaman, Zia Ush Tarzi, Chaimaa Turrini, Paolo Ufeoshi, Grace Ibukunoluwa Vargas-Perez, Victor A. Di Stefano, Alessandro Powers, Simon T. Han, The Anh |
| contents | AI safety is an increasingly urgent concern as the capabilities and adoption of AI systems grow. Existing evolutionary models of AI governance have primarily examined incentives for safe development and effective regulation, typically representing users' trust as a one-shot adoption choice rather than as a dynamic, evolving process shaped by repeated interactions. We instead model trust as reduced monitoring in a repeated, asymmetric interaction between users and AI developers, where checking AI behaviour is costly. Using evolutionary game theory, we study how user trust strategies and developer choices between safe (compliant) and unsafe (non-compliant) AI co-evolve under different levels of monitoring cost and institutional regimes. We complement the infinite-population replicator analysis with stochastic finite-population dynamics and reinforcement learning (Q-learning) simulations. Across these approaches, we find three robust long-run regimes: no adoption with unsafe development, unsafe but widely adopted systems, and safe systems that are widely adopted. Only the last is desirable, and it arises when penalties for unsafe behaviour exceed the extra cost of safety and users can still afford to monitor at least occasionally. Our results formally support governance proposals that emphasise transparency, low-cost monitoring, and meaningful sanctions, and they show that neither regulation alone nor blind user trust is sufficient to prevent evolutionary drift towards unsafe or low-adoption outcomes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_24742 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Trust as Monitoring: Evolutionary Dynamics of User Trust and AI Developer Behaviour Bashir, Adeela Song, Zhao Ogbo, Ndidi Bianca Balabanova, Nataliya Smit, Martin Leung, Chin-wing Bova, Paolo Serrano, Manuel Chica Dissanayake, Dhanushka Duong, Manh Hong Domingos, Elias Fernandez Huber-Kralj, Nikita Krellner, Marcus Powell, Andrew Sarkadi, Stefan Santos, Fernando P. Shamszaman, Zia Ush Tarzi, Chaimaa Turrini, Paolo Ufeoshi, Grace Ibukunoluwa Vargas-Perez, Victor A. Di Stefano, Alessandro Powers, Simon T. Han, The Anh Artificial Intelligence Machine Learning Multiagent Systems Adaptation and Self-Organizing Systems AI safety is an increasingly urgent concern as the capabilities and adoption of AI systems grow. Existing evolutionary models of AI governance have primarily examined incentives for safe development and effective regulation, typically representing users' trust as a one-shot adoption choice rather than as a dynamic, evolving process shaped by repeated interactions. We instead model trust as reduced monitoring in a repeated, asymmetric interaction between users and AI developers, where checking AI behaviour is costly. Using evolutionary game theory, we study how user trust strategies and developer choices between safe (compliant) and unsafe (non-compliant) AI co-evolve under different levels of monitoring cost and institutional regimes. We complement the infinite-population replicator analysis with stochastic finite-population dynamics and reinforcement learning (Q-learning) simulations. Across these approaches, we find three robust long-run regimes: no adoption with unsafe development, unsafe but widely adopted systems, and safe systems that are widely adopted. Only the last is desirable, and it arises when penalties for unsafe behaviour exceed the extra cost of safety and users can still afford to monitor at least occasionally. Our results formally support governance proposals that emphasise transparency, low-cost monitoring, and meaningful sanctions, and they show that neither regulation alone nor blind user trust is sufficient to prevent evolutionary drift towards unsafe or low-adoption outcomes. |
| title | Trust as Monitoring: Evolutionary Dynamics of User Trust and AI Developer Behaviour |
| topic | Artificial Intelligence Machine Learning Multiagent Systems Adaptation and Self-Organizing Systems |
| url | https://arxiv.org/abs/2603.24742 |