mlsanityData to 7 Oct 2026

Towards Explainability in Legal Outcome Prediction Models

arXiv 2403.16852 ↗First posted 25 Mar 2024cs.CL cs.AIcited by 4

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Abstract (arXiv)

Current legal outcome prediction models - a staple of legal NLP - do not explain their reasoning. However, to employ these models in the real world, human legal actors need to be able to understand the model's decisions. In the case of common law, legal practitioners reason towards the outcome of a case by referring to past case law, known as precedent. We contend that precedent is, therefore, a natural way of facilitating explainability for legal NLP models. In this paper, we contribute a novel method for identifying the precedent employed by legal outcome prediction models. Furthermore, by developing a taxonomy of legal precedent, we are able to compare human judges and neural models with respect to the different types of precedent they rely on. We find that while the models learn to predict outcomes reasonably well, their use of precedent is unlike that of human judges.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 27 of its 57 references that are in the map. First posted on arXiv 25 Mar 2024.

Builds on

27 of 57 references.

References · 27

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15 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Gender Bias in Explainability: Investigating Performance Disparity in Post-hoc Methodscited by 3
  2. 2025LLMs for Legal Subsumption in German Employment Contractscited by 0
  3. 2025When Explainability Meets Privacy: An Investigation at the Intersection of Post-hoc Explainability and Differential Privacy in the Context of Natural Language Processingcited by 0
  4. 2026Focus-LIME: Surgical Interpretation of Long-Context Large Language Models via Proxy-Based Neighborhood Selectioncited by 0