Good and safe uses of AI Oracles
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Abstract (arXiv)
It is possible that powerful and potentially dangerous artificial intelligence (AI) might be developed in the future. An Oracle is a design which aims to restrain the impact of a potentially dangerous AI by restricting the agent to no actions besides answering questions. Unfortunately, most Oracles will be motivated to gain more control over the world by manipulating users through the content of their answers, and Oracles of potentially high intelligence might be very successful at this citepDBLP:journals/corr/AlfonsecaCACAR16. In this paper we present two designs for Oracles which, even under pessimistic assumptions, will not manipulate their users into releasing them and yet will still be incentivised to provide their users with helpful answers. The first design is the counterfactual Oracle -- which choses its answer as if it expected nobody to ever read it. The second design is the low-bandwidth Oracle -- which is limited by the quantity of information it can transmit.
Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Nov 2017.
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Citing papers per year: 2021: 3; 2022: 2; 2023: 3; 2024: 2; 2025: 2; 2026: 2.
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- 2021Alignment of Language Agentscited by 168
- 2021Goal Misgeneralization in Deep Reinforcement Learningcited by 123
- 2024Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systemscited by 96
- 2023Evaluating Superhuman Models with Consistency Checkscited by 47
- 2025General agents contain world modelscited by 17
- 2023Conditioning Predictive Models: Risks and Strategiescited by 11
- 2023Incentivizing honest performative predictions with proper scoring rulescited by 9
- 2026From AGI to ASIcited by 4
- 2022Scoring Rules for Performative Binary Predictioncited by 3
- 2022Negative Human Rights as a Basis for Long-term AI Safety and Regulationcited by 3
- 2021Counterfactual Planning in AGI Systemscited by 2
- 2024Joint Scoring Rules: Zero-Sum Competition Avoids Performative Predictioncited by 1
- 2026Unbiased Canonical Set-Valued Oracles Via Lattice Theory24 Jun 2026
- 2026From AGI to ASI10 Jun 2026
- 2025General agents contain world models2 Jun 2025
- 2025Statistical Scenario Modelling and Lookalike Distributions for Multi-Variate AI Risk20 Feb 2025
- 2024Joint Scoring Rules: Zero-Sum Competition Avoids Performative Prediction30 Dec 2024
- 2024Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems10 May 2024
- 2023Evaluating Superhuman Models with Consistency Checks16 Jun 2023
- 2023Incentivizing honest performative predictions with proper scoring rules28 May 2023
- 2023Conditioning Predictive Models: Risks and Strategies2 Feb 2023
- 2022Negative Human Rights as a Basis for Long-term AI Safety and Regulation31 Aug 2022
- 2022Scoring Rules for Performative Binary Prediction5 Jul 2022
- 2021Goal Misgeneralization in Deep Reinforcement Learning28 May 2021
2 more
- 2021Alignment of Language Agents26 Mar 2021
- 2021Counterfactual Planning in AGI Systems29 Jan 2021
- 2024Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systemscited by 96
- 2025General agents contain world modelscited by 17
- 2021Alignment of Language Agentscited by 168
- 2021Goal Misgeneralization in Deep Reinforcement Learningcited by 123
- 2023Evaluating Superhuman Models with Consistency Checkscited by 47
- 2023Conditioning Predictive Models: Risks and Strategiescited by 11
- 2023Incentivizing honest performative predictions with proper scoring rulescited by 9
- 2026From AGI to ASIcited by 4
- 2022Scoring Rules for Performative Binary Predictioncited by 3
- 2022Negative Human Rights as a Basis for Long-term AI Safety and Regulationcited by 3
- 2021Counterfactual Planning in AGI Systemscited by 2
- 2024Joint Scoring Rules: Zero-Sum Competition Avoids Performative Predictioncited by 1
- 2025Statistical Scenario Modelling and Lookalike Distributions for Multi-Variate AI Riskcited by 1
- 2026Unbiased Canonical Set-Valued Oracles Via Lattice Theorycited by 1
- 2026Unbiased Canonical Set-Valued Oracles Via Lattice Theory24 Jun 2026
- 2026From AGI to ASI10 Jun 2026
- 2025Statistical Scenario Modelling and Lookalike Distributions for Multi-Variate AI Risk20 Feb 2025
- 2024Joint Scoring Rules: Zero-Sum Competition Avoids Performative Prediction30 Dec 2024
- 2023Evaluating Superhuman Models with Consistency Checks16 Jun 2023
- 2023Incentivizing honest performative predictions with proper scoring rules28 May 2023
- 2023Conditioning Predictive Models: Risks and Strategies2 Feb 2023
- 2022Negative Human Rights as a Basis for Long-term AI Safety and Regulation31 Aug 2022
- 2022Scoring Rules for Performative Binary Prediction5 Jul 2022
- 2021Goal Misgeneralization in Deep Reinforcement Learning28 May 2021
- 2021Alignment of Language Agents26 Mar 2021
- 2021Counterfactual Planning in AGI Systems29 Jan 2021