8 citations · 13 across the 8 of their papers we have counts for
6 papers · 1 filter
Towards Explainable Adjudicative Variance: Quantifying Judicial Discretion via Gated Multi-Task Learning
Stanisław 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…
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).…
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…
Automatic Information Extraction From Employment Tribunal Judgements Using Large Language Models
Joana Ribeiro de Faria, Huiyuan Xie, Felix Steffek
Court transcripts and judgments are rich repositories of legal knowledge, detailing the intricacies of cases and the rationale behind judicial decisions. The extraction of key info…
LLM vs. Lawyers: Identifying a Subset of Summary Judgments in a Large UK Case Law Dataset
Ahmed Izzidien, Holli Sargeant, Felix Steffek
To undertake computational research of the law, efficiently identifying datasets of court decisions that relate to a specific legal issue is a crucial yet challenging endeavour. Th…
The Cambridge Law Corpus: A Dataset for Legal AI Research
Andreas Östling, Holli Sargeant, Huiyuan Xie +5
We introduce the Cambridge Law Corpus (CLC), a dataset for legal AI research. It consists of over 250 000 court cases from the UK. Most cases are from the 21st century, but the cor…