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Requirements for Tax XAI Under Constitutional Principles and Human Rights

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Explainable and Transparent AI and Multi-Agent Systems (EXTRAAMAS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13283))

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Abstract

Tax authorities worldwide make extensive use of artificial intelligence (AI) technologies to automate various aspects of their tasks, such as answering taxpayer questions, assessing fraud risk, risk profiling, and auditing (selecting tax inspections). Since this automation has led to concerns about the impact of non-explainable AI systems on taxpayers’ rights, explainable AI (XAI) technologies appear to be fundamental for the lawful use of AI in the tax domain. This paper provides an initial map of the explainability requirements that AI systems must meet for tax applications. To this end, the paper examines the constitutional principles that guide taxation in democracies and the specific human rights system of the European Convention of Human Rights (ECHR), as interpreted by the European Court of Human Rights (ECtHR). Based on these requirements, the paper suggests how approaches to XAI might be deployed to address the specific needs of the various stakeholders in the tax domain.

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Notes

  1. 1.

    In the broader context of public administratione, a recent report by the Netherlands Court of Audit analysed 9 AI systems used by the Dutch government, concluding that only 3 of them met minimum audit standards for governance and accountability, data and model management, and privacy protection: [14].

  2. 2.

    A recent report from the Organisation for Economic Co-operation and Development (OECD) [8] used the term “Tax Administration 3.0” to mark a new stage of digitalization, in which taxation is moved closer to taxable events through built-in, automated compliance mechanisms and the interconnection between tax authority systems and the systems taxpayers use to run their businesses.

  3. 3.

    Article 150, I, with a few exceptions presented in the same article As specified by the legislation implementing this constitutional provision, the prohibition encompasses any changes to the constitutive elements of a tax, such as the tax rate, the base, the triggering event or the applicable penalties (Article 97 National Tax Code).

  4. 4.

    See, for example, the discussion of the Slovak constitutional case law below in this sub-section.

  5. 5.

    This article deals with opacity stemming from technological factors, but opacity may also arise due to non-technological factors such as legislative opacity: [20].

  6. 6.

    On opacity and its relation to algorithmic accountability, see [21].

  7. 7.

    This judgment is in our view rightly considered by Philip Baker as one of the biggest failings of the ECtHR, since it practically means that under the ECHR, a taxpayer in member States of the ECHR does not have right “in an ordinary tax dispute to a fair trial by an independent and impartial tribunal” [32].

  8. 8.

    Also, it is not unlikely that the ECtHR will revise the Ferrazzini case in the near future by allowing the application of Art. 6 of the ECHR to “normal” tax disputes, including those arising out of AI systems in tax law. In doing so, the Court would approach its treatment of tax disputes to the treatment of social security contributions, which are deemed to have private law features that outweigh the public elements of the obligation: [44]

  9. 9.

    It is worth to note that in an unrelated case from 2021, the Amsterdam District Court recognized, for the first time in Europe, a right to an explanation regarding an automated decision, based on the GDPR [51,52,53].

  10. 10.

    The ECtHR indicates that the scope of protection under Art. 8 of the ECHR includes only personal data processing which concerns data regarding people’s private lives, or if data processing is extensive. Hence, not all personal data is covered by Art. 8 of the ECHR [54].

  11. 11.

    A general prohibition of discrimination was enshrined in Article 1 of Protocol 12 of the ECHR. The protocol has already been ratified by enough signatories to come into effect, but nevertheless a considerable number of parties to the Convention have not ratified it.

  12. 12.

    See, however, the discussion of [55] above.

  13. 13.

    In most tax applications, AI systems deal with largely numerical data about relevant financial elements, which means that explanation approaches based on originally numerical features can play a crucial role. Nevertheless, some systems—such as those relying on text data or directly producing decisions that need to be grounded on legal arguments—might require the combination between explanation techniques and justification-based approaches for showing how the actions taken with basis on the explained outcomes can be sustained from the perspective of legal argumentation.

  14. 14.

    Depending on what a particular stakeholder is tasked with doing, they are likely to require a different kind of knowledge to do it and, thus, to seek a different kind of explanation: [73].

  15. 15.

    For a deeper discussion on this point, consult [75].

  16. 16.

    By analogy to financial risk in loan risk assessment systems, as discussed in [73].

  17. 17.

    Again, [75] can be consulted for more in-depth description and analysis.

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Acknowledgements

The authors would like to thank Anca Radu, Réka Markovich, and three anonymous EXTRAAMAS reviewers for their feedback on this paper. The authors, however, bear full responsibility for the paper.

All authors acknowledge that this paper is based upon work supported in whole by The Notre Dame-IBM Tech Ethics Lab. Such support does not constitute an endorsement by the sponsor of the authors’ views expressed in this publication.

Błażej Kuźniacki acknowledges that his work on this paper has been developed within the framework of the Amsterdam Centre for Tax Law (ACTL) research project “Designing the tax system for a cashless, platform-based and technology-driven society” (CPT project). The CPT project is financed with University funding and funds provided by external stakeholders (i.e. businesses and governments) interested in supporting academic research to design fair, efficient and fraud-proof tax systems. For more information about the CPT project and its partners, please visit its website https://actl.uva.nl/cpt-project/cpt-project.html. The support received by the author within the framework of the CPT project does not constitute an endorsement by the sponsors of the views expressed in this publication by the author.

Marco Almada would also like to thank Fundacion Carolina for granting him a doctoral scholarship, under which his work on this project was partially funded.

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Authors and Affiliations

  1. University of Amsterdam, 1012 WX, Amsterdam, Netherlands

    Blazej Kuzniacki

  2. European University Institute, Villa Salviati, 50139, Firenze, FI, Italy

    Marco Almada

  3. Mishcon de Reya, 70 Kingsway, London, WC2B 6AH, UK

    Kamil Tyliński

  4. Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, ul. Tyniecka 15/17, 02-630, Warszawa, Poland

    Łukasz Górski

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  1. University of Applied Sciences and Arts of Western Switzerland, Sierre, Switzerland

    Davide Calvaresi

  2. Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg

    Amro Najjar

  3. Victoria University of Wellington, Wellington, New Zealand

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  4. Umeå University, Umeå, Sweden

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Kuzniacki, B., Almada, M., Tyliński, K., Górski, Ł. (2022). Requirements for Tax XAI Under Constitutional Principles and Human Rights. In: Calvaresi, D., Najjar, A., Winikoff, M., Främling, K. (eds) Explainable and Transparent AI and Multi-Agent Systems. EXTRAAMAS 2022. Lecture Notes in Computer Science(), vol 13283. Springer, Cham. https://doi.org/10.1007/978-3-031-15565-9_14

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