# Principles to Practices for Responsible AI: Closing the Gap

arXiv: https://arxiv.org/abs/2006.04707 · HTML: https://mlsanity.com/paper/2006.04707/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2020.

## Built on by (most cited first, 25 of 25)

- [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023)
- [Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics](https://mlsanity.com/paper/2202.08792/) (2022)
- [Farsight: Fostering Responsible AI Awareness During AI Application Prototyping](https://mlsanity.com/paper/2402.15350/) (2024)
- [Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering](https://mlsanity.com/paper/2209.04963/) (2022)
- [Co-designing an AI Impact Assessment Report Template with AI Practitioners and AI Compliance Experts](https://mlsanity.com/paper/2407.17374/) (2024)
- [Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions](https://mlsanity.com/paper/2107.09546/) (2021)
- [Towards Ethical Personal AI Applications: Practical Considerations for AI Assistants with Long-Term Memory](https://mlsanity.com/paper/2409.11192/) (2024)
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- [FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelines](https://mlsanity.com/paper/2307.15475/) (2023)
- [Unravelling Responsibility for AI](https://mlsanity.com/paper/2308.02608/) (2023)
- [SoUnD Framework: Analyzing (So)cial Representation in (Un)structured (D)ata](https://mlsanity.com/paper/2311.17259/) (2023)
- [Accountable Agents in Software Engineering: An Analysis of Terms of Service and a Research Roadmap](https://mlsanity.com/paper/2605.04532/) (2026)
- [Prioritizing Policies for Furthering Responsible Artificial Intelligence in the United States](https://mlsanity.com/paper/2212.00740/) (2022)
- [The Equitable AI Research Roundtable (EARR): Towards Community-Based Decision Making in Responsible AI Development](https://mlsanity.com/paper/2303.08177/) (2023)
- [Developing and Evaluating a Design Method for Positive Artificial Intelligence](https://mlsanity.com/paper/2402.01499/) (2024)
- ["With Great Power Comes Great Responsibility!": Student and Instructor Perspectives on the influence of LLMs on Undergraduate Engineering Education](https://mlsanity.com/paper/2309.10694/) (2023)
- [REFRESH: Responsible and Efficient Feature Reselection Guided by SHAP Values](https://mlsanity.com/paper/2403.08880/) (2024)
- [The AI Policy Module: Developing Computer Science Student Competency in AI Ethics and Policy](https://mlsanity.com/paper/2506.15639/) (2025)
- [Deciding how to respond: A deliberative framework to guide policymaker responses to AI systems](https://mlsanity.com/paper/2508.03666/) (2025)
- [Economic Competition, EU Regulation, and Executive Orders: A Framework for Discussing AI Policy Implications in CS Courses](https://mlsanity.com/paper/2509.25524/) (2025)
- [Quality Model for Machine Learning Components](https://mlsanity.com/paper/2602.05043/) (2026)
- [Front-End Ethics for Sensor-Fused Health Conversational Agents: An Ethical Design Space for Biometrics](https://mlsanity.com/paper/2604.06203/) (2026)
- [How Researchers Navigate Accountability, Transparency, and Trust When Using AI Tools in Early-Stage Research: A Think-Aloud Study](https://mlsanity.com/paper/2604.23136/) (2026)
- [A Critical Analysis of Trustworthy AI Tools, Mark Frameworks, and the Implementation Chasms](https://mlsanity.com/paper/2607.15480/) (2026)

## Abstract (arXiv)

Companies have considered adoption of various high-level artificial intelligence (AI) principles for responsible AI, but there is less clarity on how to implement these principles as organizational practices. This paper reviews the principles-to-practices gap. We outline five explanations for this gap ranging from a disciplinary divide to an overabundance of tools. In turn, we argue that an impact assessment framework which is broad, operationalizable, flexible, iterative, guided, and participatory is a promising approach to close the principles-to-practices gap. Finally, to help practitioners with applying these recommendations, we review a case study of AI's use in forest ecosystem restoration, demonstrating how an impact assessment framework can translate into effective and responsible AI practices.