Three Laws Blog: Law and Artificial Intelligence

de la Feria and Grau Ruiz on The Robotisation of Tax Administration @delaFeriaR

Rita de la Feria, University of Leeds, and Maria Amparo Grau Ruiz, Completense University of Madrid, are publishing The Robotisation of Tax Administration as Chapter 20 of Interactive Robotics: Legal, Ethical, Social and Economic Aspects (A. Grau, ed., Springer Nature, 2022). Here is the abstract.

Developments over the last decade in the use of AI in tax administration have been nothing short of outstanding. Not only are taxpayers increasingly making use of automated systems in tax compliance, but perhaps more importantly, tax enforcement is increasingly reliant on new technologies as compliance-enhancing and fraud-prevention tools. However, whilst the use of AI brings very significant advantages to both the efficiency and the equity of tax systems, it also carries important risks. This paper identifies the development of a new AI fallacy within the tax policy sphere, namely that of unconstrained success: that the use of AI in tax compliance and enforcement can compensate for the deficiencies of the tax law. This paper considers the rationale behind the development of this fallacy, in particular the political and institutional dynamics involved in the approval of new tax legislation. It concludes that, maximising the benefits of the use of AI in tax compliance and enforcement requires departure from this fallacy, and the recognition of the wider dynamics of the tax policy-administration symbiosis.

Download the chapter from SSRN at the link.

Hoffer on Tax Theory and Feral AI @OSU_Law

Stephanie R. Hoffer, Ohio State University College of Law, has published Tax Theory and Feral AI as Ohio State Public Law Working Paper No. 524 (2020). Here is the abstract.

Federal income tax law and theories that undergird it have not fully addressed the status of non-human earners, like animals or artificial intelligences. Scholarship on artificial intelligence and taxation primarily has focused on the taxation of AI’s owners, on whether AI itself should be taxed despite being owned by someone else, or on the philosophical question of taxing sentient AI. Thinking about tax in the context of AI that is neither owned by a person nor sentient, referred to here as “feral AI,” allows us to identify human biases in theoretical justifications of the income tax and to consider whether they are features or bugs. Specifically, this essay uses non-sentient feral AI as a vehicle to explore the benefit principle and welfarist theories, and suggests ways in which those ideas may produce less than ideal results in the context of taxpayers whom heuristics may cause us to dehumanize. The goal of this essay is not to suggest that feral AI should be subject to an income tax. Rather, it asks whether re-examining the tax policy canon in the context of feral AI reveals anything about human-centricity in tax law. The essay concludes that heavy reliance on human preferences in both the benefit principle and welfarist theories produces a poor fit when human preferences are weak, such as disposition of the last-earned dollars of the ultra-wealthy, or when human preferences are delegitimized by the government, such as those of marginalized groups like undocumented workers. Adopting a non-human point of view in tax policy, then, can inform our thinking about taxation outside the boundaries of what humans perceive to be the norm.

Download the article from SSRN at the link.

BInder on AI and Taxation

Nadia Braun Binder, University of Zurich Faculty of Law, has published AI and Taxation: Risk Management in Fully Automated Taxation Procedures. Here is the abstract.

On January 1, 2017, the Taxation Modernization Act entered into force in Germany. It includes regulations on fully automated taxation procedures. In order to uphold the principle of investigation in such fully automated procedures, a risk management system can be established by the tax authorities. The risk management system aims to detect risk-fraught cases, and thereby to prevent tax evasion. Cases identified as risk-fraught by the system need to be checked manually by the responsible tax official. Although the technical details of risk management systems are bound to secrecy, such systems are presumably based on artificial intelligence. If this is true, and especially if machine learning techniques, where algorithms self-optimize their programming in case of errors or false results, are involved, this could lead to legally relevant problems. Examples from outside tax law show that fundamental errors may occur in systematic assessments. Accordingly, the greatest challenge of using artificial intelligence in risk management systems is their control.

Download the article from SSRN at the link.

Kuziacki on The AI Tax Treaty Assistant: Decoding the Principal Purpose Test

Blazej Kuziacki, Ministry of Finance, has published The Artificial Intelligence Tax Treaty Assistant: Decoding the Principal Purpose Test. Here is the abstract.

In this article, the author explores and advocates the use of an artificial intelligence tax treaty assistant with regard to resolving issues and problems in respect of the principal purpose test as proposed in relation to the OECD/G20 Base Erosion and Profit Shifting initiative. As the PPT is very complex and ambiguous, what AI learns in dealing with those cases could be put to use regarding other rules of a similar degree of complexity and ambiguity and with a largely similar purpose and nature. For instance, domestic GAARs could be addressed by an approach very similar to that as presented in this article. AI could eventually be applied to all types of anti-tax avoidance legislation. By augmenting the power of AI, a very effective means of preventing tax avoidance on a global scale should be possible. The question is only whether countries and jurisdictions across the world would welcome AI. AI could help them draft the most effective anti-tax avoidance laws and enforce them accordingly. In the author’s opinion, the application of AI in the domain of international tax avoidance is likely to be a “game changer” by not only improving the work of tax advisers and tax administrations across the world, but also the performance of tax systems globally.

The full text is not available for download from SSRN.

Mazur on Taxing the Robots @orlymazur @SMULawSchool @PeppLawReview

Orly Mazur, Southern Methodist University School of Law, is publishing Taxing the Robots in volume 46 of Pepperdine Law Review. Here is the abstract.

Robots and other artificial intelligence-based technologies are increasingly outperforming humans in jobs previously thought safe from automation. This has led to growing concerns about the future of jobs, wages, economic equality and government revenues. To address these issues, there have been multiple calls around the world to tax the robots. Although the concerns that have led to the recent robot tax proposals may be valid, this Article cautions against the use of a robot tax. It argues that a tax that singles out robots is the wrong tool to address these critical issues and warns of the unintended consequences of such a tax, including limiting innovation. Rather, advances in robotics and other forms of artificial intelligence merely exacerbate the issues already caused by a tax system that under-taxes capital income and over-taxes labor income. Thus, this Article proposes tax policy measures that seek to rebalance our tax system so that capital income and labor income are taxed in parity. This Article also recommends non-tax policy measures that seek to improve the labor market, support displaced workers, and encourage innovation, because tax policy alone cannot solve all of the issues raised by the robotics revolution. Together, these changes have the potential to manage the threat of automation while also maximizing its advantages, thereby easing our transition into this new automation era.

Download the article from SSRN at the link.