Bekenbey AI: Innovative Solutions at the Intersection of Deep Learning and Law

Year 2024, Volume: 2 Issue: 2, 185 - 192, 30.12.2024

Erdicem Yücesan , Mehmet Ali Erkan , Ali Deveci , İhsan Tolga Medeni

Abstract

This research introduces a cutting-edge integration of generative artificial intelligence (AI) within the realm of law, creating a sophisticated application tailored for legal professionals, organizations, and the public. The Bekenbey AI model show cased in this study is distinguished by its substantial potential, with key performance metrics such as accuracy, precision, recall, F1- score, ROUGE, and BLEU scores illustrating its adeptness at legal analytics. The model demonstrates exceptional precision and adaptability across various legal sectors and frameworks, establishing it as an indispensable asset for modern legal challenges. The findings suggest that the Bekenbey AI proficiently handles and interprets legal texts, significantly aiding the progression of legal systems. The model’s efficiency escalates with the expansion of dataset sizes, emphasizing its capacity for extensive data analysis. Ongoing enhancements are focused on increasing the model’s precision and extending its functionality to a wider array of legal contexts. To the best of our knowledge, this study represents the first instance of combining the domains of artificial intelligence and law using real data.

Keywords

Advanced Generative AI, Deep Learning Innovations, Bekenbey AI Model, AI Applications in Law, Legal Framework Analysis, Legal Document Examination

Bekenbey AI: Derin Öğrenme ve Hukuk Alanlarında Yenilikçi Çözümler

Abstract

Bu araştırma, hukuk alanında üretken yapay zekanın son teknoloji entegrasyonunu sunarak, hukuk profesyonelleri, kuruluşlar ve halk için özel olarak tasarlanmış yenilikçi bir uygulama oluşturmaktadır. Bu çalışmada gösterilen Bekenbey AI modeli, doğruluk, kesinlik, duyarlılık, F1 puanı, ROUGE ve BLEU puanları gibi temel performans ölçütlerinin hukuki analizdeki etkilerini göstermesiyle öne çıkmaktadır. Model, çeşitli hukuk sektörleri ve çerçeveleri arasında kesinlik ve uyarlanabilirlik göstererek, modern hukuki zorluklar için vazgeçilmez bir uygulama haline gelmektedir. Bulgular, Bekenbey AI'nın hukuki metinleri ustalıkla ele aldığını ve yorumladığını ve hukuki sistemlerin ilerlemesine önemli ölçüde yardımcı olduğunu göstermektedir. Modelin verimliliği, veri kümesi boyutlarının genişlemesiyle birlikte artmakta ve kapsamlı veri analizi zorunluluğunu vurgulamaktadır. Devam eden gelişmeler, modelin kesinliğini artırmaya ve işlevselliğini daha kapsamlı bir hukuki bağlam yelpazesine genişletmeye odaklanmıştır. Bildiğimiz kadarıyla, bu çalışma gerçek verileri kullanarak yapay zeka ve hukuk alanlarını birleştirmenin ilk örneğini temsil etmektedir.

Keywords

Derin Öğrenme Yenilikleri, Gelişmiş Üretken Yapay Zeka, Bekenbey Yapay Zeka Modeli, Hukukta Yapay Zeka Uygulamaları, Hukuki Boyutta Analiz, Yasal Belge İncelemesi

References

• [1] T. Räz and C. Beisbart, “The importance of understanding deep learning,”Erkenntnis, vol. 89, no. 5, pp. 1823–1840, 2024.
• 2] “Reward Value-Based Goal Selection for Agents’ Cooperative Route Learning Without Communication in Reward and Goal Dynamism – SN Computer Science — link.springer.com.” https://link.springer.com/article/ 10.1007/s42979-020-00191-2. [Accessed 22-08-2024].
• [3]“GenAI: predicted impact at top U.S. law firms, statista.com.” https://www.statista.com/statistics/1456037/ genai- predicted-impact-at-top-us-law-firms/, 2024. [Accessed 20-08-2024].
• [4]“Balancing the scale: navigating ethical and practical challenges of artificial intelligence (AI) integration in legal practices - Discover Arti- ficial Intelligence — link.springer.com.” https://link.springer.com/article/10.1007/s44163-024- 00121-8. [Accessed 22-08-2024].
• [5] C. V. Chien and M. Kim, “Generative ai and legal aid: Results from a field study and 100 use cases to bridge the access to justice gap,” Loyola of Los Angeles Law Review, forthcoming, 2024.
• [6] “Generative Legal Minds - Harvard Law School Center on the Legal Profession — clp.law.harvard.edu.” https://clp.law.harvard.edu/ knowledge-hub/magazine/issues/generative-ai-in-the- legal-profession/ generative-legal-minds/. [Accessed 22- 08-2024].
• [7]“AI and law: ethical, legal, and socio-political implications AI&SOCIETY-link.springer.com.” https://link.springer.com/article/10.1007/s00146- 021- 01194-0. [Accessed 22-08-2024].
• [8] O. A. Montesinos López, A. Montesinos López, and J. Crossa, “Fundamentals of artificial neural networks and deep learning,” in Multivariate statistical machine learning methods for genomic prediction, pp. 379–425, Springer, 2022.
• [9] W. Q. Yan, “Convolutional neural networks and recurrent neural networks,” in Computational Methods for Deep Learning: Theory, Algo- rithms, and Implementations, pp. 69–124, Springer, 2023.
• [10] M. Kauffman and M. Soares, “Ai in legal services: New trends in ai-enabled legal services. soca, 14, 223–226,” 2020.
• [11] S. Kumari, “From chaos to order: The role of law in society,” Indian Journal of Integrated Research in Law Volume IV Issue II| ISSN, vol. 2583,p. 0538, 2024.
• [12] L. Kern, H. P. George, L. L. Evanovich, J. M. Hodnett, and J. Freeman, “A review of us policy guidance and legislation on restraint and seclusion in schools: Considerations for improvement,” Exceptional Children, p. 00144029241247032, 2024.
• [13] Y. K. Dwivedi, L. Hughes, E. Ismagilova, G. Aarts, C. Coombs, T. Crick, Y. Duan, R. Dwivedi, J. Edwards, A. Eirug, et al., “Artificial intelligence (ai): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy,” International journal of information management, vol. 57, p. 101994, 2021.
• [14] F. H. Easterbrook, “What does legislative history tell us,” Chi.-Kent L. Rev., vol. 66, p. 441, 1990.
• [15] R. D. González, V. F. Vásquez, and H. Mikkelson, “Fundamentals of court interpretation,” Theory, policy, and practice, 1991.
• [16] A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, et al., “Language models are unsupervised multitask learners,” OpenAI blog, vol. 1, no. 8, p. 9, 2019.
• [17] R. Binns, “Fairness in machine learning: Lessons from political philosophy,” in Conference on fairness, accountability and transparency, pp. 149–159, PMLR, 2018.
• [18] D. M. Katz, M. J. Bommarito, and J. Blackman, “A general approach for predicting the behavior of the supreme court of the united states,” PloS one, vol. 12, no. 4, p. e0174698, 2017.
• [19] N. Aletras, D. Tsarapatsanis, D. Preo¸tiuc-Pietro, and V. Lampos, “Predicting judicial decisions of the european court of human rights: A natural language processing perspective,” PeerJ computer science, vol. 2, p. e93, 2016.
• [20] Q. Chen, Q. Wu, J. Chen, Q. Wu, A. van den Hengel, and M. Tan, “Scripted video generation with a bottom-up generative adversarial net- work,” IEEE Transactions on Image Processing, vol. 29, pp. 7454–7467, 2020.
• [21] B. Abimbola, E. D. L. C. Marin, and Q. Tan, “Enhancing legal sentiment analysis: A cnn-lstm document-level model,” Preprints, 2024.
• [22] “Introduction for artificial intelligence and law: special issue “natural language processing for legal texts” - Artificial Intelligence and Law — link.springer.com.” https://link.springer.com/article/10.1007/s10506-019- 09251-2. [Accessed 22-08-2024].
• [23] “Evaluating Text Classification in the Legal Domain Using BERT Embeddings — link.springer.com.” https://link.springer.com/chapter/10.1007/ 978-3-031- 48232-8_6. [Accessed 22-08-2024].
• [24] “A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU — arxiv.org.” https://arxiv.org/abs/ 2305.17473. [Accessed 22-08-2024].
• [25] “Solving NoSQL database governance & compliance with CI/CD — liquibase.com.” https://www.liquibase.com/blog/ solving-nosql-database-governance-and-compliance- challenges-with-ci-cd.[Accessed 22-08-2024].
• [26] “Big data preprocessing: methods and prospects - Big Data Analytics —bdataanalytics.biomedcentral.com.” https://bdataanalytics.biomedcentral.com/articles/10.11 86/s41044-016-0014-0. [Accessed 22-08-2024].
• [27] “Papers with Code - Enriching Word Vectors with Subword Information — paperswithcode.com.” https://paperswithcode.com/paper/ enriching-word- vectors-with-subword. [Accessed 22-08-2024].
• [28] “Deep CNN-LSTM with Word Embeddings for News Headline Sarcasm Detection — link.springer.com.” https://link.springer.com/chapter/ 10.1007/978-3-030-14070-0_69. [Accessed 22-08-2024].
• [29] “Language Models and Contextualised Word Embeddings -davidsbatista.net.” https://www.davidsbatista.net/blog/2018/12/06/Word_ Embeddings/. [Accessed 22-08-2024].
• [30] “What Are Word Embeddings? | IBM — ibm.com.” https://www.ibm.com/topics/word-embeddings. [Accessed 22-08-2024].