Modeling AGI Safety Frameworks with Causal Influence Diagrams
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Abstract (arXiv)
Proposals for safe AGI systems are typically made at the level of frameworks, specifying how the components of the proposed system should be trained and interact with each other. In this paper, we model and compare the most promising AGI safety frameworks using causal influence diagrams. The diagrams show the optimization objective and causal assumptions of the framework. The unified representation permits easy comparison of frameworks and their assumptions. We hope that the diagrams will serve as an accessible and visual introduction to the main AGI safety frameworks.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jun 2019.
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Citing papers per year: 2021: 4; 2022: 3; 2023: 0; 2024: 0; 2025: 1.
8 papers.
- 2021Alignment of Language Agentscited by 167
- 2021Agent Incentives: A Causal Perspectivecited by 51
- 2022Counterfactual harmcited by 29
- 2022Worldwide AI Ethics: a review of 200 guidelines and recommendations for AI governancecited by 28
- 2021Equilibrium Refinements for Multi-Agent Influence Diagrams: Theory and Practicecited by 9
- 2022A Complete Criterion for Value of Information in Soluble Influence Diagramscited by 5
- 2025Goal-Directedness is in the Eye of the Beholdercited by 3
- 2021Counterfactual Planning in AGI Systemscited by 2
- 2025Goal-Directedness is in the Eye of the Beholder18 Aug 2025
- 2022Worldwide AI Ethics: a review of 200 guidelines and recommendations for AI governance23 Jun 2022
- 2022Counterfactual harm27 Apr 2022
- 2022A Complete Criterion for Value of Information in Soluble Influence Diagrams23 Feb 2022
- 2021Alignment of Language Agents26 Mar 2021
- 2021Equilibrium Refinements for Multi-Agent Influence Diagrams: Theory and Practice9 Feb 2021
- 2021Agent Incentives: A Causal Perspective2 Feb 2021
- 2021Counterfactual Planning in AGI Systems29 Jan 2021