@inproceedings{jeong-lee-2026-objection,
title = "{OBJECTION}! Lawyer Agents Mitigate Guilty Bias in Legal Judgment Prediction",
author = "Jeong, Jaehoon and
Lee, Jay-Yoon",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1497/",
pages = "34205--34225",
ISBN = "979-8-89176-459-0",
abstract = "Legal Judgment Prediction (LJP) models are typically trained on documents that describe facts from a prosecutorial perspective. Existing datasets further exhibit severe label imbalance toward guilty outcomes. Consequently, these models suffer from ``Guilty Bias'', blindly accepting the prosecution{'}s narrative as objective truth. Previous studies employing three-step reasoning structures or training on synthetically generated innocence data improve overall accuracy, but they still fail to mitigate bias at inference time.\par In this paper, we introduce OBJECTION, an inference-time pipeline that integrates an Adversarial Lawyer Agent into each 3-step reasoning of offense, unlawfulness, and culpability. Unlike generic critics, our agent actively challenges the model{'}s presumptions of guilt by injecting legal defense arguments at each reasoning stage. To thoroughly evaluate this, we present a new ``Natural Innocent'' dataset including 3.4k real-world cases, overcoming the limitations of synthetic innocence benchmarks. Test results show that OBJECTION drastically reduces the False Guilty Rate (FGR) from 82.93{\%} (SOTA baseline) to 16.69{\%}, proving its capability to perform substantive legal reasoning. This work denotes a key progress toward aligning Legal AI with the presumption of innocence."
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<abstract>Legal Judgment Prediction (LJP) models are typically trained on documents that describe facts from a prosecutorial perspective. Existing datasets further exhibit severe label imbalance toward guilty outcomes. Consequently, these models suffer from “Guilty Bias”, blindly accepting the prosecution’s narrative as objective truth. Previous studies employing three-step reasoning structures or training on synthetically generated innocence data improve overall accuracy, but they still fail to mitigate bias at inference time.\par In this paper, we introduce OBJECTION, an inference-time pipeline that integrates an Adversarial Lawyer Agent into each 3-step reasoning of offense, unlawfulness, and culpability. Unlike generic critics, our agent actively challenges the model’s presumptions of guilt by injecting legal defense arguments at each reasoning stage. To thoroughly evaluate this, we present a new “Natural Innocent” dataset including 3.4k real-world cases, overcoming the limitations of synthetic innocence benchmarks. Test results show that OBJECTION drastically reduces the False Guilty Rate (FGR) from 82.93% (SOTA baseline) to 16.69%, proving its capability to perform substantive legal reasoning. This work denotes a key progress toward aligning Legal AI with the presumption of innocence.</abstract>
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%0 Conference Proceedings
%T OBJECTION! Lawyer Agents Mitigate Guilty Bias in Legal Judgment Prediction
%A Jeong, Jaehoon
%A Lee, Jay-Yoon
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F jeong-lee-2026-objection
%X Legal Judgment Prediction (LJP) models are typically trained on documents that describe facts from a prosecutorial perspective. Existing datasets further exhibit severe label imbalance toward guilty outcomes. Consequently, these models suffer from “Guilty Bias”, blindly accepting the prosecution’s narrative as objective truth. Previous studies employing three-step reasoning structures or training on synthetically generated innocence data improve overall accuracy, but they still fail to mitigate bias at inference time.\par In this paper, we introduce OBJECTION, an inference-time pipeline that integrates an Adversarial Lawyer Agent into each 3-step reasoning of offense, unlawfulness, and culpability. Unlike generic critics, our agent actively challenges the model’s presumptions of guilt by injecting legal defense arguments at each reasoning stage. To thoroughly evaluate this, we present a new “Natural Innocent” dataset including 3.4k real-world cases, overcoming the limitations of synthetic innocence benchmarks. Test results show that OBJECTION drastically reduces the False Guilty Rate (FGR) from 82.93% (SOTA baseline) to 16.69%, proving its capability to perform substantive legal reasoning. This work denotes a key progress toward aligning Legal AI with the presumption of innocence.
%U https://aclanthology.org/2026.emnlp-main.1497/
%P 34205-34225
Markdown (Informal)
[OBJECTION! Lawyer Agents Mitigate Guilty Bias in Legal Judgment Prediction](https://aclanthology.org/2026.emnlp-main.1497/) (Jeong & Lee, EMNLP 2026)
ACL