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Cited by 43Artificial Intelligence in LawExplainable Artificial Intelligence (XAI)Machine Learning in Healthcare

Semi-Supervised Methods for Explainable Legal Prediction

Branting, K., Weiss, B., Brown, B., Pfeifer, C., Chakraborty, A., Ferro, L., Pfaff, M., Yeh, A.·2019·Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law
DOI 10.1145/3322640.3326723
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Abstract

Legal decision-support systems have the potential to improve access to justice, administrative efficiency, and judicial consistency, but broad adoption of such systems is contingent on development of technologies with low knowledge-engineering, validation, and maintenance costs. This paper describes two approaches to an important form of legal decision support---explainable outcome prediction---that obviate both annotation of an entire decision corpus and manual processing of new cases. The first approach, which uses an Attention Network for prediction and attention weights to highlight salient case text, was shown to be capable of predicting decisions, but attention-weight-based text highlighting did not demonstrably improve human decision speed or accuracy in an evaluation with 61 human subjects. The second approach, termed SCALE (Semi-supervised Case Annotation for Legal Explanations), exploits structural and semantic regularities in case corpora to identify textual patterns that have both predictable relationships to case decisions and explanatory value.

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Crossref · 10.1145/3322640.3326723

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  • It would in all likelihood be difficult to ensure transparency of a machine-learning ADM system so as to produce a relevant explanation to the individual of how, and on the basis of what information, the ADM had arrived at a certain conclusion.

    “it would in all likelihood be difficult to ensure transparency of a machinelearning ADM system so as to produce a relevant explanation to the individual of how, and on the basis of what information, the ADM had arrived at a certain conclusion (see Branting et al. 2019, 22”

    From Administrative due process when using automated decision-making in public administration: some notes from a Finnish perspective (2021) · cites this paper as Branting et al. 2019, 22

  • A rule-based ADM solution appears to be the only option when ADM systems are designed for administrative decision-making.

    “a rule-based ADM solution appears to be the only option when ADM systems are designed for administrative decision-making”

    From Administrative due process when using automated decision-making in public administration: some notes from a Finnish perspective (2021) · cites this paper as Branting et al. 2019

  • Decision prediction is not appropriate for completely autonomous processes because denial of benefits by an automated process raises significant due process issues, and prediction accuracy is limited by the absence of explicit modeling of legal rules or issues.

    “'decision prediction is not appropriate for completely autonomous processes' because '[d] enial of benefits by an automated process, no matter how accurate, raises significant due process issues, and in any event prediction accuracy is limited in this paradigm by the absence of explicit modeling of legal rules or issues'”

    From Administrative due process when using automated decision-making in public administration: some notes from a Finnish perspective (2021) · cites this paper as Branting et al. 2019

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Authors
Branting, K., Weiss, B., Brown, B., Pfeifer, C., Chakraborty, A., Ferro, L., Pfaff, M., & Yeh, A.
Affiliations
  • The MITRE Corporation, McLean, VA, USA
  • Mitre (United States) (US)
  • The MITRE Corporation, Ann Arbor, MI, USA
ORCID
Venue
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law
Publisher
ACM
Published
2019-06-17
Pages
22-31
DOI
10.1145/3322640.3326723
OpenAlex
W2963594477
Cited by
43
References
35
Citations by year
2025: 3 · 2024: 3 · 2023: 9 · 2022: 7 · 2021: 10 · 2020: 8 · 2019: 3
FWCI
48.24
Type
conference paper
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
International Conference on Artificial Intelligence and Law (publishedVersion)
Topics
  • Social Sciences › Political Science and International Relations › Artificial Intelligence in Law
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords
legal decision support, semi-supervised learning, attentional networks, scale, case annotations, textual patterns, judicial decision prediction

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