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The paper introduces OBJECTION, an inference-time pipeline using adversarial lawyer agents to mitigate guilty bias in legal judgment prediction models, demonstrating a significant reduction in false guilty rates and releasing a new 'Natural Innocent' dataset.
This paper empirically investigates shortcut learning in legal judgment prediction using UK Employment Tribunal claims, finding that models exploit outcome-revealing cues in post-hoc judicial texts, but performance remains robust after masking such leakage features.