# Artificial intelligence in government: Concepts, standards, and a unified framework arXiv: https://arxiv.org/abs/2210.17218 ยท HTML: https://mlsanity.com/paper/2210.17218/ ## Key facts - Cited by 3 papers in the map (data through 14 Oct 2026). - Builds on 35 of its 184 references that are in the map. - First posted on arXiv 31 Oct 2022. ## Builds on (most cited first, 30 of 35) - [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019) - [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018) - [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019) - [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017) - [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018) - [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018) - [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020) - [Methods for Interpreting and Understanding Deep Neural Networks](https://mlsanity.com/paper/1706.07979/) (2017) - [Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)](https://mlsanity.com/paper/2003.12206/) (2020) - [Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy](https://mlsanity.com/paper/2002.04087/) (2020) - [The Values Encoded in Machine Learning Research](https://mlsanity.com/paper/2106.15590/) (2021) - [Principles alone cannot guarantee ethical AI](https://mlsanity.com/paper/1906.06668/) (2019) - [The role of artificial intelligence in achieving the Sustainable Development Goals](https://mlsanity.com/paper/1905.00501/) (2019) - [Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database](https://mlsanity.com/paper/2011.08512/) (2020) - [The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies](https://mlsanity.com/paper/2007.15911/) (2020) - [The Fallacy of AI Functionality](https://mlsanity.com/paper/2206.09511/) (2022) - [Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AI](https://mlsanity.com/paper/2005.05906/) (2020) - [Understanding artificial intelligence ethics and safety](https://mlsanity.com/paper/1906.05684/) (2019) - [The social dilemma of autonomous vehicles](https://mlsanity.com/paper/1510.03346/) (2015) - [The Flaws of Policies Requiring Human Oversight of Government Algorithms](https://mlsanity.com/paper/2109.05067/) (2021) - [The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence](https://mlsanity.com/paper/2201.04200/) (2022) - [The future of human-AI collaboration: a taxonomy of design knowledge for hybrid intelligence systems](https://mlsanity.com/paper/2105.03354/) (2021) - [Society-in-the-Loop: Programming the Algorithmic Social Contract](https://mlsanity.com/paper/1707.07232/) (2017) - [Dislocated Accountabilities in the AI Supply Chain: Modularity and Developers' Notions of Responsibility](https://mlsanity.com/paper/2209.09780/) (2022) - [Fairness Perceptions of Algorithmic Decision-Making: A Systematic Review of the Empirical Literature](https://mlsanity.com/paper/2103.12016/) (2021) - [Four Years of FAccT: A Reflexive, Mixed-Methods Analysis of Research Contributions, Shortcomings, and Future Prospects](https://mlsanity.com/paper/2206.06738/) (2022) - [Citation concept analysis (CCA) - A new form of citation analysis revealing the usefulness of concepts for other researchers illustrated by two exemplary case studies including classic books by Thomas S. Kuhn and Karl R. Popper](https://mlsanity.com/paper/1905.12410/) (2019) - [Ethical Assurance: A practical approach to the responsible design, development, and deployment of data-driven technologies](https://mlsanity.com/paper/2110.05164/) (2021) - [A 20-Year Community Roadmap for Artificial Intelligence Research in the US](https://mlsanity.com/paper/1908.02624/) (2019) - [What May Visualization Processes Optimize?](https://mlsanity.com/paper/1506.02245/) (2015) ## Built on by (most cited first, 3 of 3) - [Oversight Structures for Agentic AI in Public-Sector Organizations](https://mlsanity.com/paper/2506.04836/) (2025) - [AI for bureaucratic productivity: Measuring the potential of AI to help automate 143 million UK government transactions](https://mlsanity.com/paper/2403.14712/) (2024) - [The Algorithmic State Architecture (ASA): An Integrated Framework for AI-Enabled Government](https://mlsanity.com/paper/2503.08725/) (2025) ## Abstract (arXiv) Recent advances in artificial intelligence (AI), especially in generative language modelling, hold the promise of transforming government. Given the advanced capabilities of new AI systems, it is critical that these are embedded using standard operational procedures, clear epistemic criteria, and behave in alignment with the normative expectations of society. Scholars in multiple domains have subsequently begun to conceptualize the different forms that AI applications may take, highlighting both their potential benefits and pitfalls. However, the literature remains fragmented, with researchers in social science disciplines like public administration and political science, and the fast-moving fields of AI, ML, and robotics, all developing concepts in relative isolation. Although there are calls to formalize the emerging study of AI in government, a balanced account that captures the full depth of theoretical perspectives needed to understand the consequences of embedding AI into a public sector context is lacking. Here, we unify efforts across social and technical disciplines by first conducting an integrative literature review to identify and cluster 69 key terms that frequently co-occur in the multidisciplinary study of AI. We then build on the results of this bibliometric analysis to propose three new multifaceted concepts for understanding and analysing AI-based systems for government (AI-GOV) in a more unified way: (1) operational fitness, (2) epistemic alignment, and (3) normative divergence. Finally, we put these concepts to work by using them as dimensions in a conceptual typology of AI-GOV and connecting each with emerging AI technical measurement standards to encourage operationalization, foster cross-disciplinary dialogue, and stimulate debate among those aiming to rethink government with AI.