6 papers
Shortcut Learning in Legal Judgment Prediction: Empirical Evidence from the UK Employment Tribunal
Joe Watson, Joana Ribeiro de Faria, Marcus Tomalin +6
Current Legal Judgment Prediction (LJP) is constrained by its reliance on post-hoc judicial materials, increasing the likelihood that models perform retrospective classification ra…
Towards Explainable Adjudicative Variance: Quantifying Judicial Discretion via Gated Multi-Task Learning
StanisÅaw Sójka, Felix Steffek, Matthias Grabmair
Legal outcome prediction must disentangle objective case facts from adjudicative context. Merit-based rulings rely on factual evidence while technical disposals may hinge on judici…
AIReg-Bench: Benchmarking Language Models That Assess AI Regulation Compliance
Bill Marino, Rosco Hunter, Christoph Schnabl +9
As governments move to regulate AI, there is growing interest in using Large Language Models (LLMs) to assess whether or not an AI system complies with a given AI Regulation (AIR).…
Large Language Models' Complicit Responses to Illicit Instructions across Socio-Legal Contexts
Xing Wang, Huiyuan Xie, Yiyan Wang +7
Large language models (LLMs) are now deployed at unprecedented scale, assisting millions of users in daily tasks. However, the risk of these models assisting unlawful activities re…
Topic Classification of Case Law Using a Large Language Model and a New Taxonomy for UK Law: AI Insights into Summary Judgment
Holli Sargeant, Ahmed Izzidien, Felix Steffek
This paper addresses a critical gap in legal analytics by developing and applying a novel taxonomy for topic classification of summary judgment cases in the United Kingdom. Using a…
The CLC-UKET Dataset: Benchmarking Case Outcome Prediction for the UK Employment Tribunal
Huiyuan Xie, Felix Steffek, Joana Ribeiro de Faria +2
This paper explores the intersection of technological innovation and access to justice by developing a benchmark for predicting case outcomes in the UK Employment Tribunal (UKET).…