Ignacio González García, Salvador Duart Crespo

Algorithms and Trustworthiness in Tax Administration

Introduction

Algorithms and trustworthiness in tax administration. Algorithms in tax administration offer efficiency but raise ethical, legal, and governance issues. Learn how to balance innovation, public trust, and accountability.

Abstract

This article examines the integration of algorithms into the work of Tax Administrations. It argues that these tools are not neutral instruments but reflect historical biases and institutional choices. While they offer opportunities for greater efficiency and consistency in areas such as fraud detection and service provision, their use also raises complex legal, ethical, and governance challenges. The authors explore how algorithms, particularly those based on machine learning and artificial intelligence, reshape decision-making processes. They show that although these technologies can implement efficient and trusted methods, their use as digital civil servants requires careful oversight. Through practical examples, such as VAT fraud detection and AI-assisted taxpayer guidance, the paper highlights both the potential and the risks involved. Tax professionals, the authors argue, must play a central role in defining the objectives, assessing the limitations, and ensuring the ethical use of algorithmic tools. Algorithms should serve as support systems, not replacements for legal reasoning or institutional judgment. The paper concludes that the challenge is not whether to adopt algorithms, but how to govern their use responsibly, balancing innovation with the duty to uphold public trust, fairness, and accountability.

Review

This article, "Algorithms and Trustworthiness in Tax Administration," presents a pertinent and critical examination of the escalating integration of algorithmic tools within tax administrations globally. The authors effectively challenge the perception of algorithms as neutral instruments, arguing instead that they are deeply embedded with historical biases and institutional choices. While acknowledging the tangible benefits these tools offer in enhancing efficiency and consistency, particularly in areas like fraud detection and service provision, the paper astutely highlights the complex legal, ethical, and governance challenges that arise as machine learning and artificial intelligence increasingly reshape traditional decision-making processes. A significant strength of this work lies in its detailed exploration of how these technologies redefine established administrative procedures, emphasizing the critical need for careful oversight when algorithms function as "digital civil servants." Through compelling practical examples, such as their application in VAT fraud detection and AI-assisted taxpayer guidance, the authors effectively illustrate both the transformative potential and the inherent risks. Crucially, the paper advocates for tax professionals to assume a central role in defining objectives, assessing limitations, and upholding the ethical deployment of algorithmic tools, underscoring the fundamental principle that algorithms should augment, rather than replace, human legal reasoning and institutional judgment. The article concludes by framing the central challenge not as whether to adopt algorithms, but rather how to govern their use responsibly. This nuanced and comprehensive perspective makes a valuable contribution to the ongoing discourse surrounding digitalization in public administration, offering a robust framework for balancing innovation with the fundamental duties to uphold public trust, fairness, and accountability. Its insights are essential reading for policymakers, tax administrators, legal scholars, and professionals grappling with the profound implications of AI in governance, providing a compelling call to action for proactive and ethically grounded implementation strategies.

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