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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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Zhang, Y., Feng, D., Tian, W., & Wang, H. E. (2022) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-dss-smartcity-dependsys57074.2022.00327 · Cited by 1
Legal Judgment Prediction Incorporating Guiding Cases Matching
Li, H., Cai, S., & Ming, Z. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44693-1_40 · Cited by 1
CDD: A Large Scale Dataset for Legal Intelligence Research
Ji, C., Zhang, Y., Jatowt, A., & Wu, H. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-industry.7 · Cited by 1
Indian Supreme Court Judgement Dataset for Prediction Models
Sivaranjani, N., Karthik, R., Sancith, J., & Siva, P. (2023) · International Conference on Intelligent Computing and Control for Engineering and Business Systems (ICCEBS) · doi:10.1109/iccebs58601.2023.10448659 · Cited by 1
Interpretable answer retrieval based on heterogeneous network embedding
Wu, Y., Pan, X., Li, J., Dou, S., & Wang, X. (2024) · Pattern Recognition Letters · doi:10.1016/j.patrec.2024.03.023 · Cited by 1
Artificial Intelligence Model for Legal Decision Prediction
A-Lmansouri, A. M., & Alamri, J. (2025) · Studies in systems, decision and control · doi:10.1007/978-3-031-84636-6_54 · Cited by 1
Multi-scale Heterogeneous Graph Attention Network for Prison Term Prediction
Feng, D., Hu, B., Zhang, Y., Tian, W., & Wang, H. E. (2023) · IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) · doi:10.1109/iciba56860.2023.10164974
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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