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Improving BERT-based Query-by-Document Retrieval with Multi-task Optimization Read chapter Read first chapter

2022 | OriginalPaper | Chapter

Towards Explainable Search in Legal Text

Author : Sayantan Polley

Published in: Advances in Information Retrieval

Publisher: Springer International Publishing

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Abstract

Assume a non-AI expert user like a lawyer using an AI driven text retrieval (IR) system. A user is not always sure why a certain document is at the bottom of the ranking list although it seems quite relevant and is expected at the top. Is it due to the proportion of matching terms, semantically related topics, or unknown reasons? This can be confusing and leading to lack of trust and transparency in AI systems. Explainable AI (XAI) is currently a vibrant research topic which is being investigated from various perspectives in the IR and ML community. While a major focus of the ML community is to explain a classification decision, a key focus in IR is to explain the notion of similarity that is used to estimate relevance rankings. Relevance in IR is a complex entity based on various notions of similarity (e.g. semantic, syntactic, contextual) in text. This is often subjective and ranking is an estimation of the relevance. In this work, we attempt to explore the notion of similarity in text with regard to aspects such as semantics, law cross references and arrive at interpretable facets of evidence which can be used to explain rankings. The idea is to explain non-AI experts that why a certain document is relevant to a query, for legal domain. We present our preliminary findings, outline future work and discuss challenges.

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Literature
1.
Ai, Q., Zhang, Y., Bi, K., Croft, W.B.: Explainable product search with a dynamic relation embedding model. ACM Trans. Inf. Syst. (TOIS) 38(1), 1–29 (2019)CrossRef
2.
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE 10(7), e0130140 (2015)CrossRef
3.
Bench-Capon, T., et al.: A history of ai and law in 50 papers: 25 years of the international conference on ai and law. Artificial Intelligence and Law (2012)
4.
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)CrossRef
5.
Biega, A.J., Gummadi, K.P., Weikum, G.: Equity of attention: Amortizing individual fairness in rankings. In: The 41st International ACM sigir Conference on Research & Development in Information Retrieval, pp. 405–414 (2018)
6.
Castillo, C.: Fairness and transparency in ranking. In: ACM SIGIR Forum, vol. 52. ACM New York (2019)
7.
Chios, V.: Helping results assessment by adding explainable elements to the deep relevance matching model. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (2020)
8.
Dey, A., et al.: Evaluating reliability in explainable search. In: 2021 IEEE 2nd International Conference on Human-Machine Systems (ICHMS) (2021)
9.
10.
Fernando, Z.T., Singh, J., Anand, A.: A study on the interpretability of neural retrieval models using DeepSHAP. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (2019)
11.
Gunning, D., Aha, D.: Darpa’s explainable artificial intelligence (xai) program. AI Mag. 40(2), 44–58 (2019)
12.
Hearst, M.A.: Visualization of term distribution information in full text information access. In: CHI ’95 Conference Proceedings, pp. 59–66 (1995)
13.
Hoeber, O., Brooks, M., Schroeder, D., Yang, X.D.: Thehotmap. com: enabling flexible interaction in next-generation web search interfaces. In: 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol. 1, pp. 730–734. IEEE (2008)
14.
Jackson, P., Al-Kofahi, K., Tyrrell, A., Vachher, A.: Information extraction from case law and retrieval of prior cases. Artificial Intelligence (2003)
15.
Jockers, M.L.: Macroanalysis: Digital Methods and Literary History, 1st edn. University of Illinois Press, USA (2013)CrossRef
16.
Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS (2017)
17.
Malik, V., et al.: ILDC for CJPE: Indian legal documents corpus for court judgment prediction and explanation. In: Proceedings of the 59th Annual Meeting of the ACL (2021)
18.
Marchionini, G.: Exploratory search: from finding to understanding. Communications of the ACM (2006)
19.
Mencia, E.L., Fürnkranz, J.: Efficient multilabel classification algorithms for large-scale problems in the legal domain. In: Semantic Processing of Legal Texts, pp. 192–215. Springer (2010)
20.
Mi, S., Jiang, J.: Understanding the interpretability of search result summaries. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 989–992 (2019)
21.
Mohseni, S., Zarei, N., Ragan, E.D.: A multidisciplinary survey and framework for design and evaluation of explainable ai systems. ACM Trans. Interactive Intell. Syst. (TiiS) 11(3–4), 1–45 (2021)
22.
Polley, S., Ghosh, S., Thiel, M., Kotzyba, M., Nürnberger, A.: Simfic: an explainable book search companion. In: Proceedings of the IEEE International Conference on Human-Machine Systems (ICHMS) (2020)
23.
Polley, S., Janki, A., Thiel, M., Hoebel-Mueller, J., Nuernberger, A.: Exdocs: evidence based explainable document search. In: Workshop on Causality in Search and Recommendations co-located with 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (2021)
24.
25.
Polley, S., Koparde, R.R., Gowri, A.B., Perera, M., Nuernberger, A.: Towards trustworthiness in the context of explainable search. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2580–2584 (2021)
26.
27.
28.
Roy, D., Saha, S., Mitra, M., Sen, B., Ganguly, D.: I-REX: a lucene plugin for explainable IR. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management (2019)
29.
Shao, Y.: Towards legal case retrieval. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (2020)
30.
Singh, J., Anand, A.: Exs: explainable search using local model agnostic interpretability. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 770–773 (2019)
31.
Singh, J., Anand, A.: Model agnostic interpretability of rankers via intent modelling. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 618–628 (2020)
32.
33.
Van Opijnen, M., Santos, C.: On the concept of relevance in legal information retrieval. Kluwer Academic Publishers (2017)
34.
Zhang, Z., Rudra, K., Anand, A.: Explain and predict, and then predict again. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp. 418–426 (2021)
Metadata
Title
Towards Explainable Search in Legal Text
Author
Sayantan Polley
Copyright Year
2022
Book
Advances in Information Retrieval

Print ISBN: 978-3-030-99738-0

Electronic ISBN: 978-3-030-99739-7

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-3-030-99739-7_65