Defining AI in Policy versus Practice
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Abstract (arXiv)
Recent concern about harms of information technologies motivate consideration of regulatory action to forestall or constrain certain developments in the field of artificial intelligence (AI). However, definitional ambiguity hampers the possibility of conversation about this urgent topic of public concern. Legal and regulatory interventions require agreed-upon definitions, but consensus around a definition of AI has been elusive, especially in policy conversations. With an eye towards practical working definitions and a broader understanding of positions on these issues, we survey experts and review published policy documents to examine researcher and policy-maker conceptions of AI. We find that while AI researchers favor definitions of AI that emphasize technical functionality, policy-makers instead use definitions that compare systems to human thinking and behavior. We point out that definitions adhering closely to the functionality of AI systems are more inclusive of technologies in use today, whereas definitions that emphasize human-like capabilities are most applicable to hypothetical future technologies. As a result of this gap, ethical and regulatory efforts may overemphasize concern about future technologies at the expense of pressing issues with existing deployed technologies.
Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Dec 2019.
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Citing papers per year: 2021: 1; 2022: 5; 2023: 2; 2024: 4; 2025: 2; 2026: 1.
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- 2022The Fallacy of AI Functionalitycited by 88
- 2022Trust in AI and Its Role in the Acceptance of AI Technologiescited by 29
- 2022Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desideratacited by 28
- 2022From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible MLcited by 27
- 2023Explainability in AI Policies: A Critical Review of Communications, Reports, Regulations, and Standards in the EU, US, and UKcited by 13
- 2022Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliancecited by 7
- 2024AI on AI: Exploring the Utility of GPT as an Expert Annotator of AI Publicationscited by 4
- 2024(Beyond) Reasonable Doubt: Challenges that Public Defenders Face in Scrutinizing AI in Courtcited by 3
- 2023Machine Learning practices and infrastructurescited by 2
- 2025Reduced AI Acceptance After the Generative AI Boom: Evidence From a Two-Wave Survey Studycited by 2
- 2021MAIR: Framework for mining relationships between research articles, strategies, and regulations in the field of explainable artificial intelligencecited by 1
- 2024Social Science Is Necessary for Operationalizing Socially Responsible Foundation Modelscited by 1
3 more
- 2026Xiaomi-TabLDM: A Tabular Foundation Model Technical Report3 Sep 2026
- 2025Reduced AI Acceptance After the Generative AI Boom: Evidence From a Two-Wave Survey Study27 Oct 2025
- 2025One Bad NOFO? AI Governance in Federal Grantmaking13 May 2025
- 2024Social Science Is Necessary for Operationalizing Socially Responsible Foundation Models20 Dec 2024
- 2024The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems7 Jul 2024
- 2024AI on AI: Exploring the Utility of GPT as an Expert Annotator of AI Publications14 Mar 2024
- 2024(Beyond) Reasonable Doubt: Challenges that Public Defenders Face in Scrutinizing AI in Court13 Mar 2024
- 2023Machine Learning practices and infrastructures13 Jul 2023
- 2023Explainability in AI Policies: A Critical Review of Communications, Reports, Regulations, and Standards in the EU, US, and UK20 Apr 2023
- 2022From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible ML6 Oct 2022
- 2022The Fallacy of AI Functionality20 Jun 2022
- 2022Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliance10 Jun 2022
3 more
- 2022Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata6 Jun 2022
- 2022Trust in AI and Its Role in the Acceptance of AI Technologies23 Mar 2022
- 2021MAIR: Framework for mining relationships between research articles, strategies, and regulations in the field of explainable artificial intelligence29 Jul 2021
- 2022The Fallacy of AI Functionalitycited by 88
- 2022Trust in AI and Its Role in the Acceptance of AI Technologiescited by 29
- 2022Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desideratacited by 28
- 2022From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible MLcited by 27
- 2023Explainability in AI Policies: A Critical Review of Communications, Reports, Regulations, and Standards in the EU, US, and UKcited by 13
- 2022Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliancecited by 7
- 2024AI on AI: Exploring the Utility of GPT as an Expert Annotator of AI Publicationscited by 4
- 2024(Beyond) Reasonable Doubt: Challenges that Public Defenders Face in Scrutinizing AI in Courtcited by 3
- 2023Machine Learning practices and infrastructurescited by 2
- 2025Reduced AI Acceptance After the Generative AI Boom: Evidence From a Two-Wave Survey Studycited by 2
- 2021MAIR: Framework for mining relationships between research articles, strategies, and regulations in the field of explainable artificial intelligencecited by 1
- 2024Social Science Is Necessary for Operationalizing Socially Responsible Foundation Modelscited by 1
12 most cited of 15 All 15 (JSON)
- 2026Xiaomi-TabLDM: A Tabular Foundation Model Technical Report3 Sep 2026
- 2025Reduced AI Acceptance After the Generative AI Boom: Evidence From a Two-Wave Survey Study27 Oct 2025
- 2025One Bad NOFO? AI Governance in Federal Grantmaking13 May 2025
- 2024Social Science Is Necessary for Operationalizing Socially Responsible Foundation Models20 Dec 2024
- 2024The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems7 Jul 2024
- 2024AI on AI: Exploring the Utility of GPT as an Expert Annotator of AI Publications14 Mar 2024
- 2024(Beyond) Reasonable Doubt: Challenges that Public Defenders Face in Scrutinizing AI in Court13 Mar 2024
- 2023Machine Learning practices and infrastructures13 Jul 2023
- 2023Explainability in AI Policies: A Critical Review of Communications, Reports, Regulations, and Standards in the EU, US, and UK20 Apr 2023
- 2022From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible ML6 Oct 2022
- 2022The Fallacy of AI Functionality20 Jun 2022
- 2022Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliance10 Jun 2022
12 newest of 15 All 15 (JSON)