@inproceedings{lee-chae-2026-outside,
title = "Outside the Four Corners: Charting the Missing Evidence in {K}orean {AI} Training-Data License Screening",
author = "Lee, Jiwoo and
Chae, Sungwon",
editor = "Aletras, Nikolaos and
Chalkidis, Ilias and
Barrett, Leslie and
Goanț{\u{a}}, C{\u{a}}t{\u{a}}lina and
Preoțiuc-Pietro, Daniel and
Spanakis, Gerasimos and
Xu, Shanshan",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.nllp-1.37/",
pages = "432--455",
ISBN = "979-8-89176-494-1",
abstract = "As training corpora grow, screening licenses for AI training data at scale is likely to fall to large language models, but a screening system must distinguish when a document settles a proposed use from when the answer depends on facts outside it. We formalize this distinction under Korean copyright law. Supreme Court of Korea authority supports five types of extrinsic fact that may be required to decide whether a grant reaches a use or binds the rightsholder: consideration, usage of trade, the rightsholder{'}s conduct, market substitution, and upstream title. A practicing Korean intellectual property lawyer applies this framework to Korean licenses, terms of service, and standard-form contracts, yielding a benchmark of whether each document settles five AI-related uses and, if not, which facts are missing. We evaluate frontier proprietary, open-weight, and Korean-specialized models under increasingly detailed legal procedures and broader retrieved context. Neither the lawyer{'}s screening procedure, additional context, nor even the governing clause reliably improves models' ability to distinguish determinate from indeterminate cases; the governing clause instead increases abstention on many determinate items. These interventions mainly shift decision thresholds rather than discriminability, a difference obscured by aggregate accuracy. Automated license screening has two limits: some questions are indeterminate from the instrument because resolving them requires facts outside it, and models over-abstain on questions the document does settle."
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<abstract>As training corpora grow, screening licenses for AI training data at scale is likely to fall to large language models, but a screening system must distinguish when a document settles a proposed use from when the answer depends on facts outside it. We formalize this distinction under Korean copyright law. Supreme Court of Korea authority supports five types of extrinsic fact that may be required to decide whether a grant reaches a use or binds the rightsholder: consideration, usage of trade, the rightsholder’s conduct, market substitution, and upstream title. A practicing Korean intellectual property lawyer applies this framework to Korean licenses, terms of service, and standard-form contracts, yielding a benchmark of whether each document settles five AI-related uses and, if not, which facts are missing. We evaluate frontier proprietary, open-weight, and Korean-specialized models under increasingly detailed legal procedures and broader retrieved context. Neither the lawyer’s screening procedure, additional context, nor even the governing clause reliably improves models’ ability to distinguish determinate from indeterminate cases; the governing clause instead increases abstention on many determinate items. These interventions mainly shift decision thresholds rather than discriminability, a difference obscured by aggregate accuracy. Automated license screening has two limits: some questions are indeterminate from the instrument because resolving them requires facts outside it, and models over-abstain on questions the document does settle.</abstract>
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%0 Conference Proceedings
%T Outside the Four Corners: Charting the Missing Evidence in Korean AI Training-Data License Screening
%A Lee, Jiwoo
%A Chae, Sungwon
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goanță, Cătălina
%Y Preoțiuc-Pietro, Daniel
%Y Spanakis, Gerasimos
%Y Xu, Shanshan
%S Proceedings of the Natural Legal Language Processing Workshop 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-494-1
%F lee-chae-2026-outside
%X As training corpora grow, screening licenses for AI training data at scale is likely to fall to large language models, but a screening system must distinguish when a document settles a proposed use from when the answer depends on facts outside it. We formalize this distinction under Korean copyright law. Supreme Court of Korea authority supports five types of extrinsic fact that may be required to decide whether a grant reaches a use or binds the rightsholder: consideration, usage of trade, the rightsholder’s conduct, market substitution, and upstream title. A practicing Korean intellectual property lawyer applies this framework to Korean licenses, terms of service, and standard-form contracts, yielding a benchmark of whether each document settles five AI-related uses and, if not, which facts are missing. We evaluate frontier proprietary, open-weight, and Korean-specialized models under increasingly detailed legal procedures and broader retrieved context. Neither the lawyer’s screening procedure, additional context, nor even the governing clause reliably improves models’ ability to distinguish determinate from indeterminate cases; the governing clause instead increases abstention on many determinate items. These interventions mainly shift decision thresholds rather than discriminability, a difference obscured by aggregate accuracy. Automated license screening has two limits: some questions are indeterminate from the instrument because resolving them requires facts outside it, and models over-abstain on questions the document does settle.
%U https://aclanthology.org/2026.nllp-1.37/
%P 432-455
Markdown (Informal)
[Outside the Four Corners: Charting the Missing Evidence in Korean AI Training-Data License Screening](https://aclanthology.org/2026.nllp-1.37/) (Lee & Chae, NLLP 2026)
ACL