# Actionable Principles for Artificial Intelligence Policy: Three Pathways

arXiv: https://arxiv.org/abs/2102.12406 · HTML: https://mlsanity.com/paper/2102.12406/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2021.

## Built on by (most cited first, 5 of 5)

- [Establishing and Evaluating Trustworthy AI: Overview and Research Challenges](https://mlsanity.com/paper/2411.09973/) (2024)
- [A Review of the Ethics of Artificial Intelligence and its Applications in the United States](https://mlsanity.com/paper/2310.05751/) (2023)
- [Fairness: from the ethical principle to the practice of Machine Learning development as an ongoing agreement with stakeholders](https://mlsanity.com/paper/2304.06031/) (2023)
- [Framework, Standards, Applications and Best practices of Responsible AI : A Comprehensive Survey](https://mlsanity.com/paper/2504.13979/) (2025)
- [An Evaluation Framework for National AI Regulation](https://mlsanity.com/paper/2608.15417/) (2026)

## Abstract (arXiv)

In the development of governmental policy for artificial intelligence (AI) that is informed by ethics, one avenue currently pursued is that of drawing on AI Ethics Principles. However, these AI Ethics Principles often fail to be actioned in governmental policy. This paper proposes a novel framework for the development of Actionable Principles for AI. The approach acknowledges the relevance of AI Ethics Principles and homes in on methodological elements to increase their practical implementability in policy processes. As a case study, elements are extracted from the development process of the Ethics Guidelines for Trustworthy AI of the European Commissions High Level Expert Group on AI. Subsequently, these elements are expanded on and evaluated in light of their ability to contribute to a prototype framework for the development of Actionable Principles for AI. The paper proposes the following three propositions for the formation of such a prototype framework: (1) preliminary landscape assessments; (2) multi-stakeholder participation and cross-sectoral feedback; and, (3) mechanisms to support implementation and operationalizability.