# Defining AI in Policy versus Practice

arXiv: https://arxiv.org/abs/1912.11095 · HTML: https://mlsanity.com/paper/1912.11095/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2019.

## Built on by (most cited first, 15 of 15)

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- [Trust in AI and Its Role in the Acceptance of AI Technologies](https://mlsanity.com/paper/2203.12687/) (2022)
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- [From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible ML](https://mlsanity.com/paper/2210.03535/) (2022)
- [Explainability in AI Policies: A Critical Review of Communications, Reports, Regulations, and Standards in the EU, US, and UK](https://mlsanity.com/paper/2304.11218/) (2023)
- [Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliance](https://mlsanity.com/paper/2207.01482/) (2022)
- [AI on AI: Exploring the Utility of GPT as an Expert Annotator of AI Publications](https://mlsanity.com/paper/2403.09097/) (2024)
- [(Beyond) Reasonable Doubt: Challenges that Public Defenders Face in Scrutinizing AI in Court](https://mlsanity.com/paper/2403.13004/) (2024)
- [Machine Learning practices and infrastructures](https://mlsanity.com/paper/2307.06518/) (2023)
- [Reduced AI Acceptance After the Generative AI Boom: Evidence From a Two-Wave Survey Study](https://mlsanity.com/paper/2510.23578/) (2025)
- [MAIR: Framework for mining relationships between research articles, strategies, and regulations in the field of explainable artificial intelligence](https://mlsanity.com/paper/2108.06216/) (2021)
- [Social Science Is Necessary for Operationalizing Socially Responsible Foundation Models](https://mlsanity.com/paper/2412.16355/) (2024)
- [One Bad NOFO? AI Governance in Federal Grantmaking](https://mlsanity.com/paper/2505.08133/) (2025)
- [The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems](https://mlsanity.com/paper/2407.05341/) (2024)
- [Xiaomi-TabLDM: A Tabular Foundation Model Technical Report](https://mlsanity.com/paper/2609.03880/) (2026)

## 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.