Definitions of intent suitable for algorithms
Not on the map. Why?
Abstract (arXiv)
Intent modifies an actor's culpability of many types wrongdoing. Autonomous Algorithmic Agents have the capability of causing harm, and whilst their current lack of legal personhood precludes them from committing crimes, it is useful for a number of parties to understand under what type of intentional mode an algorithm might transgress. From the perspective of the creator or owner they would like ensure that their algorithms never intend to cause harm by doing things that would otherwise be labelled criminal if committed by a legal person. Prosecutors might have an interest in understanding whether the actions of an algorithm were internally intended according to a transparent definition of the concept. The presence or absence of intention in the algorithmic agent might inform the court as to the complicity of its owner. This article introduces definitions for direct, oblique (or indirect) and ulterior intent which can be used to test for intent in an algorithmic actor.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 6 of its 122 references that are in the map. First posted on arXiv 8 Jun 2021.
Builds on
6 of 122 references.
References · 6
- 2016Concrete Problems in AI Safetycited by 1,642
- 2017Generative Adversarial Networks: An Overviewcited by 590
- 2018The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communitiescited by 78
- 2018Towards Formal Definitions of Blameworthiness, Intention, and Moral Responsibilitycited by 32
- 2018Explicability? Legibility? Predictability? Transparency? Privacy? Security? The Emerging Landscape of Interpretable Agent Behaviorcited by 30
- 2021Extending counterfactual accounts of intent to include oblique intentcited by 2
Built on by
Citing papers per year: 2022: 1; 2023: 3; 2024: 3; 2025: 0; 2026: 1.
8 papers.
- 2024AI Sandbagging: Language Models can Strategically Underperform on Evaluationscited by 145
- 2023Honesty Is the Best Policy: Defining and Mitigating AI Deceptioncited by 40
- 2024The Reasons that Agents Act: Intention and Instrumental Goalscited by 23
- 2026Intelligent AI Delegationcited by 18
- 2023Human Control: Definitions and Algorithmscited by 10
- 2022What is Proxy Discrimination?cited by 7
- 2023Experiments with Detecting and Mitigating AI Deceptioncited by 0
- 2024Evaluating Language Model Character Traitscited by 0
- 2026Intelligent AI Delegation12 Feb 2026
- 2024Evaluating Language Model Character Traits5 Oct 2024
- 2024AI Sandbagging: Language Models can Strategically Underperform on Evaluations11 Jun 2024
- 2024The Reasons that Agents Act: Intention and Instrumental Goals11 Feb 2024
- 2023Honesty Is the Best Policy: Defining and Mitigating AI Deception3 Dec 2023
- 2023Experiments with Detecting and Mitigating AI Deception26 Jun 2023
- 2023Human Control: Definitions and Algorithms31 May 2023
- 2022What is Proxy Discrimination?11 May 2022