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This paper identifies a critical methodological void in legal AI evaluation, noting that current benchmarks fail to measure doctrinal reasoning—the core interpretive skill required by the EU AI Act for high-risk judicial applications. Bridging this gap is essential before deploying large language models in courts where "appropriate accuracy" is legally mandated rather than just statistically desirable.
Surse care acoperăarXiv, LessWrong, Quantum Zeitgeist, Quantum Computing Report
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The Measurement Gap in the Automation of EU Law: Benchmarking Doctrinal Legal Reasoning under the EU AI Act

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2606.18158v1 Announce Type: cross Abstract: Large language models now produce legal text of at least median quality, yet no existing benchmark can evaluate whether they perform doctrinal legal reasoning, which forms the interpretive core of legal work, rather than the ancillary, paralegal tasks that most current legal-AI evaluations measure. This measurement gap is not only methodological but legal: the EU AI Act makes "appropriate accuracy" a binding requirement for high-risk AI used in the judicial domain, yet that requirement cannot acquire operational content without the very doctrinal-reasoning benchmark the field lacks.

Abstract:Large language models now produce legal text of at least median quality, yet no existing benchmark can evaluate whether they perform doctrinal legal reasoning, which forms the interpretive core of legal work, rather than the ancillary, paralegal tasks that most current legal-AI evaluations measure. This measurement gap is not only methodological but legal: the EU AI Act makes "appropriate accuracy" a binding requirement for high-risk AI used in the judicial domain, yet that requirement cannot acquire operational content without the very doctrinal-reasoning benchmark the field lacks.

Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2606.18158 [cs.CY]

(or arXiv:2606.18158v1 [cs.CY] for this version)

https://doi.org/10.48550/arXiv.2606.18158 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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