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Bridging Legal Doctrine and Data Science: Machine Learning for the Detection of “Artificial Arrangements” under EU Tax Law

Zuzanna Jagła

European Taxation2026https://doi.org/10.59403/2yndvqearticle
ABDC B
Weight
0.50

Abstract

Tax avoidance exploits legal loopholes, costing governments billions – USD 348 billion in 2024 alone. EU law fights back with the “artificial arrangement” doctrine, but vague criteria have led to inconsistent application by the courts. This article merges the legal world with the world of computer science, where insufficiently clear doctrines can be resolved by computational thinking and machines. When lex ferenda is not achieved, it is worth considering solutions beyond traditional legal reform because justice can be served through innovation.

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https://doi.org/https://doi.org/10.59403/2yndvqe

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@article{zuzanna2026,
  title        = {{Bridging Legal Doctrine and Data Science: Machine Learning for the Detection of “Artificial Arrangements” under EU Tax Law}},
  author       = {Zuzanna Jagła},
  journal      = {European Taxation},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.59403/2yndvqe},
}

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Bridging Legal Doctrine and Data Science: Machine Learning for the Detection of “Artificial Arrangements” under EU Tax Law

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.