ARCHIVES
Year 2026 · Volume 5 · Issue 3
Original Article
Clause Guard: An Explainable Contract Clause Risk Analyzer with Evidence-Linked Findings and Human Review
Veerendeswari J1
Mangaiyarkarasi A2
Ahamed Thasheem A3
Dinesh J4
Sathya Seelan S5
Rohith M6
1 Assistant Professor & HoD, Department of Information Technology, Rajiv Gandhi College of Engineering and Technology, Puducherry, India 2 Assistant Professor, Department of Information Technology, Rajiv Gandhi College of Engineering and Technology, Puducherry, India 3 4 5 6 UG Scholar, Department of Information Technology, Rajiv Gandhi College of Engineering and Technology, Puducherry, India.
Published Online: September-December 2026
Pages: 337-347
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260503040
References
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[7]. D. Tuggener, P. von Däniken, T. Peetz and M. Cieliebak, “LEDGAR: A large-scale multi-label corpus for text classification of legal provisions in contracts,” in Proc. 12th Language Resources and Evaluation Conf. (LREC), Marseille, France, 2020.
[8]. I. Chalkidis, A. Jana, D. Hartung, M. Bommarito, I. Androutsopoulos, D. M. Katz and N. Aletras, “LexGLUE: A benchmark dataset for legal language understanding in English,” in Proc. 60th Annu. Meeting of the Association for Computational Linguistics (ACL), 2022, pp. 4310–4330.
[9]. E. Mosqueira-Rey, E. Hernández-Pereira, D. Alonso-Ríos, J. Bobes-Bascarán and Á. Fernández-Leal, “Human-in-the-loop machine learning: A state of the art,” Artificial Intelligence Review, vol. 56, no. 4, pp. 3005–3054, 2023.
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[12]. S. H. Wang, A. Scardigli, L. Tang, W. Chen, D. Levkin, A. Chen, S. Ball, T. Woodside, O. Zhang and D. Hendrycks, “MAUD: An expert-annotated legal NLP dataset for merger agreement understanding,” in Proc. 2023 Conf. Empirical Methods in Natural Language Processing (EMNLP), Singapore, 2023, pp. 16369–16382.
[13]. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems 30 (NeurIPS), 2017, pp. 5998–6008.
[14]. J. Devlin, M.-W. Chang, K. Lee and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proc. NAACL-HLT, 2019, pp. 4171–4186.
[15]. I. Chalkidis, M. Fergadiotis, P. Malakasiotis, N. Aletras and I. Androutsopoulos, “LEGAL-BERT: The Muppets straight out of law school,” in Findings of the Association for Computational Linguistics: EMNLP 2020, 2020, pp. 2898–2904.
[16]. N. Guha, J. Nyarko, D. E. Ho, C. Ré, A. Chilton et al., “LegalBench: A collaboratively built benchmark for measuring legal reasoning in large language models,” in Advances in Neural Information Processing Systems 36 (NeurIPS), Datasets and Benchmarks Track, 2023, pp. 44123–44279.
[17]. D. M. Katz, D. Hartung, L. Gerlach, A. Jana and M. J. Bommarito II, “Natural language processing in the legal domain,” arXiv preprint arXiv:2302.12039, 2023.
[18]. C. Rudin, “Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,” Nature Machine Intelligence, vol. 1, no. 5, pp. 206–215, 2019.
[19]. L. Chiticariu, Y. Li and F. R. Reiss, “Rule-based information extraction is dead! Long live rule-based information extraction systems!” in Proc. 2013 Conf. Empirical Methods in Natural Language Processing (EMNLP), Seattle, WA, USA, 2013, pp. 827–832.
[20]. L. Luu, D.-H. Chu, H. Olickel, P. Saxena and A. Hobor, “Making smart contracts smarter,” in Proc. ACM SIGSAC Conf. Computer and Communications Security (CCS), 2016, pp. 254–269.
[21]. J. Feist, G. Grieco and A. Groce, “Slither: A static analysis framework for smart contracts,” in Proc. IEEE/ACM 2nd Int. Workshop on Emerging Trends in Software Engineering for Blockchain (WETSEB), 2019, pp. 8–15.
[22]. R. Smith, “An overview of the Tesseract OCR engine,” in Proc. 9th Int. Conf. Document Analysis and Recognition (ICDAR), 2007, pp. 629–633.
[23]. Vercel, “Next.js documentation.” [Online]. Available: https://nextjs.org/docs (accessed Oct. 2026).
[24]. S. Ramírez, “FastAPI documentation.” [Online]. Available: https://fastapi.tiangolo.com/ (accessed Oct. 2026).
[25]. C. Percival and S. Josefsson, “The scrypt password-based key derivation function,” IETF RFC 7914, Aug. 2016.
[26]. Microsoft, “Playwright documentation.” [Online]. Available: https://playwright.dev/ (accessed Oct. 2026).
[2]. M. Dahl, V. Magesh, M. Suzgun and D. E. Ho, “Large legal fictions: Profiling legal hallucinations in large language models,” Journal of Legal Analysis, vol. 16, p. 64, 2024.
[3]. European Parliament and Council of the European Union, “Regulation (EU) 2016/679 (General Data Protection Regulation),” Official Journal of the European Union, vol. L 119, pp. 1–88, May 2016.
[4]. Government of India, Ministry of Law and Justice, “The Digital Personal Data Protection Act, 2023 (No. 22 of 2023),” The Gazette of India, Aug. 2023.
[5]. M. Lippi, P. Pałka, G. Contissa, F. Lagioia, H.-W. Micklitz, G. Sartor and P. Torroni, “CLAUDETTE: An automated detector of potentially unfair clauses in online terms of service,” Artificial Intelligence and Law, vol. 27, no. 2, pp. 117–139, 2019.
[6]. D. Hendrycks, C. Burns, A. Chen and S. Ball, “CUAD: An expert-annotated NLP dataset for legal contract review,” in Proc. NeurIPS Datasets and Benchmarks Track, 2021.
[7]. D. Tuggener, P. von Däniken, T. Peetz and M. Cieliebak, “LEDGAR: A large-scale multi-label corpus for text classification of legal provisions in contracts,” in Proc. 12th Language Resources and Evaluation Conf. (LREC), Marseille, France, 2020.
[8]. I. Chalkidis, A. Jana, D. Hartung, M. Bommarito, I. Androutsopoulos, D. M. Katz and N. Aletras, “LexGLUE: A benchmark dataset for legal language understanding in English,” in Proc. 60th Annu. Meeting of the Association for Computational Linguistics (ACL), 2022, pp. 4310–4330.
[9]. E. Mosqueira-Rey, E. Hernández-Pereira, D. Alonso-Ríos, J. Bobes-Bascarán and Á. Fernández-Leal, “Human-in-the-loop machine learning: A state of the art,” Artificial Intelligence Review, vol. 56, no. 4, pp. 3005–3054, 2023.
[10]. F. Ruggeri, F. Lagioia, M. Lippi and P. Torroni, “Detecting and explaining unfairness in consumer contracts through memory networks,” Artificial Intelligence and Law, vol. 30, pp. 59–92, 2022, doi: 10.1007/s10506-021-09288-2.
[11]. Y. Koreeda and C. D. Manning, “ContractNLI: A dataset for document-level natural language inference for contracts,” in Findings of the Association for Computational Linguistics: EMNLP 2021, 2021, pp. 1907–1919.
[12]. S. H. Wang, A. Scardigli, L. Tang, W. Chen, D. Levkin, A. Chen, S. Ball, T. Woodside, O. Zhang and D. Hendrycks, “MAUD: An expert-annotated legal NLP dataset for merger agreement understanding,” in Proc. 2023 Conf. Empirical Methods in Natural Language Processing (EMNLP), Singapore, 2023, pp. 16369–16382.
[13]. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems 30 (NeurIPS), 2017, pp. 5998–6008.
[14]. J. Devlin, M.-W. Chang, K. Lee and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proc. NAACL-HLT, 2019, pp. 4171–4186.
[15]. I. Chalkidis, M. Fergadiotis, P. Malakasiotis, N. Aletras and I. Androutsopoulos, “LEGAL-BERT: The Muppets straight out of law school,” in Findings of the Association for Computational Linguistics: EMNLP 2020, 2020, pp. 2898–2904.
[16]. N. Guha, J. Nyarko, D. E. Ho, C. Ré, A. Chilton et al., “LegalBench: A collaboratively built benchmark for measuring legal reasoning in large language models,” in Advances in Neural Information Processing Systems 36 (NeurIPS), Datasets and Benchmarks Track, 2023, pp. 44123–44279.
[17]. D. M. Katz, D. Hartung, L. Gerlach, A. Jana and M. J. Bommarito II, “Natural language processing in the legal domain,” arXiv preprint arXiv:2302.12039, 2023.
[18]. C. Rudin, “Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,” Nature Machine Intelligence, vol. 1, no. 5, pp. 206–215, 2019.
[19]. L. Chiticariu, Y. Li and F. R. Reiss, “Rule-based information extraction is dead! Long live rule-based information extraction systems!” in Proc. 2013 Conf. Empirical Methods in Natural Language Processing (EMNLP), Seattle, WA, USA, 2013, pp. 827–832.
[20]. L. Luu, D.-H. Chu, H. Olickel, P. Saxena and A. Hobor, “Making smart contracts smarter,” in Proc. ACM SIGSAC Conf. Computer and Communications Security (CCS), 2016, pp. 254–269.
[21]. J. Feist, G. Grieco and A. Groce, “Slither: A static analysis framework for smart contracts,” in Proc. IEEE/ACM 2nd Int. Workshop on Emerging Trends in Software Engineering for Blockchain (WETSEB), 2019, pp. 8–15.
[22]. R. Smith, “An overview of the Tesseract OCR engine,” in Proc. 9th Int. Conf. Document Analysis and Recognition (ICDAR), 2007, pp. 629–633.
[23]. Vercel, “Next.js documentation.” [Online]. Available: https://nextjs.org/docs (accessed Oct. 2026).
[24]. S. Ramírez, “FastAPI documentation.” [Online]. Available: https://fastapi.tiangolo.com/ (accessed Oct. 2026).
[25]. C. Percival and S. Josefsson, “The scrypt password-based key derivation function,” IETF RFC 7914, Aug. 2016.
[26]. Microsoft, “Playwright documentation.” [Online]. Available: https://playwright.dev/ (accessed Oct. 2026).
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