# Artificial intelligence, rationalization, and the limits of control in the public sector: the case of tax policy optimization

arXiv: https://arxiv.org/abs/2407.05336 · HTML: https://mlsanity.com/paper/2407.05336/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 100 references that are in the map.
- First posted on arXiv 7 Jul 2024.

## Builds on (most cited first, 8 of 8)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020)
- [Reinforcement learning](https://mlsanity.com/paper/2405.10369/) (2024)
- [The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence](https://mlsanity.com/paper/2002.06177/) (2020)
- [Algorithmic Monoculture and Social Welfare](https://mlsanity.com/paper/2101.05853/) (2021)
- [Learning to Perform Physics Experiments via Deep Reinforcement Learning](https://mlsanity.com/paper/1611.01843/) (2016)
- [Institutionalising Ethics in AI through Broader Impact Requirements](https://mlsanity.com/paper/2106.11039/) (2021)
- [Four Years of FAccT: A Reflexive, Mixed-Methods Analysis of Research Contributions, Shortcomings, and Future Prospects](https://mlsanity.com/paper/2206.06738/) (2022)

## 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)
- [Embodied AI: Emerging Risks and Opportunities for Policy Action](https://mlsanity.com/paper/2509.00117/) (2025)
- [A Technical Typology of AI Systems in Public Administration](https://mlsanity.com/paper/2606.31755/) (2026)

## Abstract (arXiv)

The use of artificial intelligence (AI) in the public sector is best understood as a continuation and intensification of long standing rationalization and bureaucratization processes. Drawing on Weber, we take the core of these processes to be the replacement of traditions with instrumental rationality, i.e., the most calculable and efficient way of achieving any given policy objective. In this article, we demonstrate how much of the criticisms, both among the public and in scholarship, directed towards AI systems spring from well known tensions at the heart of Weberian rationalization. To illustrate this point, we introduce a thought experiment whereby AI systems are used to optimize tax policy to advance a specific normative end, reducing economic inequality. Our analysis shows that building a machine-like tax system that promotes social and economic equality is possible. However, it also highlights that AI driven policy optimization (i) comes at the exclusion of other competing political values, (ii) overrides citizens sense of their noninstrumental obligations to each other, and (iii) undermines the notion of humans as self-determining beings. Contemporary scholarship and advocacy directed towards ensuring that AI systems are legal, ethical, and safe build on and reinforce central assumptions that underpin the process of rationalization, including the modern idea that science can sweep away oppressive systems and replace them with a rule of reason that would rescue humans from moral injustices. That is overly optimistic. Science can only provide the means, they cannot dictate the ends. Nonetheless, the use of AI in the public sector can also benefit the institutions and processes of liberal democracies. Most importantly, AI driven policy optimization demands that normative ends are made explicit and formalized, thereby subjecting them to public scrutiny and debate.