Lessons from complexity theory for AI governance
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866909519478521856 |
|---|---|
| author | Kolt, Noam Shur-Ofry, Michal Cohen, Reuven |
| author_facet | Kolt, Noam Shur-Ofry, Michal Cohen, Reuven |
| contents | The study of complex adaptive systems, pioneered in physics, biology, and the social sciences, offers important lessons for AI governance. Contemporary AI systems and the environments in which they operate exhibit many of the properties characteristic of complex systems, including nonlinear growth patterns, emergent phenomena, and cascading effects that can lead to tail risks. Complexity theory can help illuminate the features of AI that pose central challenges for policymakers, such as feedback loops induced by training AI models on synthetic data and the interconnectedness between AI systems and critical infrastructure. Drawing on insights from other domains shaped by complex systems, including public health and climate change, we examine how efforts to govern AI are marked by deep uncertainty. To contend with this challenge, we propose a set of complexity-compatible principles concerning the timing and structure of AI governance, and the risk thresholds that should trigger regulatory intervention. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_00012 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Lessons from complexity theory for AI governance Kolt, Noam Shur-Ofry, Michal Cohen, Reuven Computers and Society The study of complex adaptive systems, pioneered in physics, biology, and the social sciences, offers important lessons for AI governance. Contemporary AI systems and the environments in which they operate exhibit many of the properties characteristic of complex systems, including nonlinear growth patterns, emergent phenomena, and cascading effects that can lead to tail risks. Complexity theory can help illuminate the features of AI that pose central challenges for policymakers, such as feedback loops induced by training AI models on synthetic data and the interconnectedness between AI systems and critical infrastructure. Drawing on insights from other domains shaped by complex systems, including public health and climate change, we examine how efforts to govern AI are marked by deep uncertainty. To contend with this challenge, we propose a set of complexity-compatible principles concerning the timing and structure of AI governance, and the risk thresholds that should trigger regulatory intervention. |
| title | Lessons from complexity theory for AI governance |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2502.00012 |