Low Impact Artificial Intelligences
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Abstract (arXiv)
There are many goals for an AI that could become dangerous if the AI becomes superintelligent or otherwise powerful. Much work on the AI control problem has been focused on constructing AI goals that are safe even for such AIs. This paper looks at an alternative approach: defining a general concept of `low impact'. The aim is to ensure that a powerful AI which implements low impact will not modify the world extensively, even if it is given a simple or dangerous goal. The paper proposes various ways of defining and grounding low impact, and discusses methods for ensuring that the AI can still be allowed to have a (desired) impact despite the restriction. The end of the paper addresses known issues with this approach and avenues for future research.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 May 2017.
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Citing papers per year: 2018: 1; 2019: 0; 2020: 0; 2021: 2; 2022: 1; 2023: 3; 2024: 0; 2025: 1; 2026: 3.
11 papers.
- 2018Scalable agent alignment via reward modeling: a research directioncited by 447
- 2023TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AIcited by 26
- 2026Analyzing and Improving Chain-of-Thought Monitorability Through Information Theorycited by 10
- 2023Designing Fiduciary Artificial Intelligencecited by 8
- 2021Combining Reward Information from Multiple Sourcescited by 7
- 2025AI Agents Should be Regulated Based on the Extent of Their Autonomous Operationscited by 2
- 2021Challenges for Using Impact Regularizers to Avoid Negative Side Effectscited by 0
- 2022Improving performance in multi-objective decision-making in Bottles environments with soft maximin approachescited by 0
- 2023Low impact agency: review and discussioncited by 0
- 2026Shutdown Safety Valves for Advanced AIcited by 0
- 2026Calibrating Conservatism for Scalable Oversightcited by 0
- 2026Calibrating Conservatism for Scalable Oversight27 May 2026
- 2026Shutdown Safety Valves for Advanced AI7 Mar 2026
- 2026Analyzing and Improving Chain-of-Thought Monitorability Through Information Theory20 Feb 2026
- 2025AI Agents Should be Regulated Based on the Extent of Their Autonomous Operations7 Feb 2025
- 2023Designing Fiduciary Artificial Intelligence27 Jul 2023
- 2023TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI12 Jun 2023
- 2023Low impact agency: review and discussion6 Mar 2023
- 2022Improving performance in multi-objective decision-making in Bottles environments with soft maximin approaches8 Aug 2022
- 2021Combining Reward Information from Multiple Sources22 Mar 2021
- 2021Challenges for Using Impact Regularizers to Avoid Negative Side Effects29 Jan 2021
- 2018Scalable agent alignment via reward modeling: a research direction19 Nov 2018