Shortcut Learning in Legal Judgment Prediction: Empirical Evidence from the UK Employment Tribunal
Current Legal Judgment Prediction (LJP) is constrained by its reliance on post-hoc judicial materials, increasing the likelihood that models perform retrospective classification rather than true forecasting. This paper empirically investigates shortcut learning in this context by studying claim-level outcome prediction in UK Employment Tribunal (UKET) decisions. Using a corpus of 33,158 individual claims, we predict outcomes from claim texts and LLM-extracted case summaries, evaluating models ranging from interpretable TF-IDF-based classifiers to black-box LLMs. While headline predictive performance figures appear strong, we demonstrate that such performance in LJP systems trained on post...
| Authors | Joe Watson, Joana Ribeiro de Faria, Marcus Tomalin et al. |
|---|---|
| Primary category | cs.AI |
| Published | 5 July 2026 |
| Last updated at source | 10 July 2026 |
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