The DOI matches its Crossref record; 39 of the 40 references it cites were found in a registry or an index.
Iteratively Questioning and Answering for Interpretable Legal Judgment Prediction
Open accessAbstract
Legal Judgment Prediction (LJP) aims to predict judgment results according to the facts of cases. In recent years, LJP has drawn increasing attention rapidly from both academia and the legal industry, as it can provide references for legal practitioners and is expected to promote judicial justice. However, the research to date usually suffers from the lack of interpretability, which may lead to ethical issues like inconsistent judgments or gender bias. In this paper, we present QAjudge, a model based on reinforcement learning to visualize the prediction process and give interpretable judgments. QAjudge follows two essential principles in legal systems across the world: Presumption of Innocence and Elemental Trial. During inference, a Question Net will select questions from the given set and an Answer Net will answer the question according to the fact description. Finally, a Predict Net will produce judgment results based on the answers. Reward functions are designed to minimize the number of questions asked. We conduct extensive experiments on several real-world datasets. Experimental results show that QAjudge can provide interpretable judgments while maintaining comparable performance with other state-of-the-art LJP models. The codes can be found from https://github.com/thunlp/QAjudge.
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“With the aim of providing an explanation for legal judgment predictions generated by Legal Judgement Prediction (LJP), Zhong et al. [71] designed the QAjudge.”
From Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2024) · cites this paper as [71]
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Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence
Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M., & Hussain, A. (2024) · Cognitive Computation · doi:10.1007/s12559-023-10179-8 · Cited by 2,495
“With the aim of providing an explanation for legal judgment predictions generated by Legal Judgement Prediction (LJP), Zhong et al. [71] designed the QAjudge.”
LexGLUE: A Benchmark Dataset for Legal Language Understanding in English
Chalkidis, I., Jana, A., Hartung, D., Bommarito, M. J., Androutsopoulos, I., Katz, D. M., & Αλέτρας, Ν. (2021) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.297 · Cited by 511
How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence
Zhong, H., Xiao, C., Tu, C., Zhang, T., Liu, Z., & Sun, M. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.466 · Cited by 485
Self-citationLarge language models in law: A survey
Lai, J., Gan, W., Wu, J., Qi, Z., & Yu, P. S. (2024) · AI Open · doi:10.1016/j.aiopen.2024.09.002 · Cited by 263
ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation
Malik, V., Sanjay, R., Nigam, S. K., Ghosh, K., Guha, S. K., Bhattacharya, A., & Modi, A. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.313 · Cited by 198
Metadata
- Authors
- Zhong, H., Wang, Y., Tu, C., Zhang, T., Liu, Z., & Sun, M.
- Affiliations
- Tsinghua University (CN)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2020-04-03
- Volume
- 34
- Issue
- 01
- Pages
- 1250-1257
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2997429021
- Cited by
- 113
- References
- 40
- Citations by year
- 2026: 8 · 2025: 16 · 2024: 17 · 2023: 30 · 2022: 18 · 2021: 21 · 2020: 3
- FWCI
- 84.97
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- www.aaai.org/ (unspecified)
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Funders
- National Natural Science Foundation of China (61661146007, 61572273)
- National Key Research and Development Program of China (2018YFC0831900)
- Topics
- Social Sciences › Law › Artificial Intelligence in Law
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Law › Legal processes and jurisprudence
- Keywords
- legal judgment prediction, interpretability, reinforcement learning, question answering, presumption of innocence, gender bias, judicial justice
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