Analysing the Cognitive Proficiencies of Artificial Intelligence within the Legal Paradigm: Prospects within the Jurisdiction of India

Year : 2024 | Volume :11 | Issue : 01 | Page : –
By

    Indra Vijay Singh

  1. Ranvijay Singh

  2. Akash Singh

  3. Sujit Tewari

  1. Associate Professor, Department of Artificial Intelligence and Machine learning, Moodlakatte Institute of Technology, Kundapur, Karnataka, India
  2. Advocate, High Court of Karnataka, Bengaluru, Karnataka, India
  3. Student, Department of Law, Shree Geet Law College, Lucknow, Uttar Pradesh, India
  4. Student, Department of Law, Shree Geet Law College, Lucknow, Uttar Pradesh, India

Abstract

The swift progress of artificial intelligence (AI) has become a pivotal factor in various industries, notably affecting the legal sector. This study extensively investigates the substantial effects of AI on legal research and case analysis, mapping out the progression of AI technology within the legal domain. The exploration goes beyond mere acknowledgment of AI’s presence, delving into a nuanced analysis of its potential advantages and the formidable challenges it poses to traditional legal research methodologies. In examining the landscape of AI-powered tools and algorithms, the paper scrutinizes their capacity to augment and support legal professionals in case analysis, research endeavors, and the generation of valuable insights. This comprehensive review is essential in understanding how AI is not just a technological innovation but a potential game-changer in the efficiency and depth of legal processes. Additionally, the research diligently tackles ethical concerns linked to incorporating AI in the legal sector. It emphasizes the essential requirement for a well-balanced approach that integrates human expertise with AI capabilities, promoting responsible and fair implementation. Through a comprehensive exploration of existing AI applications, this paper aims to present a comprehensive and perceptive view of the considerable transformation AI is instigating in legal research and case analysis, indicating noteworthy implications for the future of the legal profession.

Keywords: Artificial Intelligence, Analysis, Ethical Scrutiny, Human Expertise, AI Capacities, Ramifications, Legal Practice.

[This article belongs to Journal of Artificial Intelligence Research & Advances(joaira)]

How to cite this article: Indra Vijay Singh, Ranvijay Singh, Akash Singh, Sujit Tewari.Analysing the Cognitive Proficiencies of Artificial Intelligence within the Legal Paradigm: Prospects within the Jurisdiction of India.Journal of Artificial Intelligence Research & Advances.2024; 11(01):-.
How to cite this URL: Indra Vijay Singh, Ranvijay Singh, Akash Singh, Sujit Tewari , Analysing the Cognitive Proficiencies of Artificial Intelligence within the Legal Paradigm: Prospects within the Jurisdiction of India joaira 2024 {cited 2024 Apr 24};11:-. Available from: https://journals.stmjournals.com/joaira/article=2024/view=144200


References

  1. Levitin, Introduction to the design and analysis of algorithms, III and. New York, Pearson, 2011, 13 p.
  1. Longo, G. Scorza, Intelligenza artificiale: l’impatto sulle nostre vite, diritti e libertà, Milano, Mondadori Università, 2020, 21 p.; M. Vimalkumar, A. Gupta, D. Sharma, Y. Dwivedi, Understanding the Effect that Task Complexity has on Automation Potential and Opacity: Implications for Algorithmic Fairness, in “AIS Transactions on Human-Computer Interaction”, 2021, v. 13, n. 1, 107 p.
  2. Levitin, Introduction to the design and analysis of algorithms, cit., 13 p.
  3. Mittelstadt, P. Allo, M. Taddeo, S. Wachter, L. Floridi, The ethics of algorithms: Mapping the debate, in “Big Data & Society”, 2016, n. 3, pp. 1-21.
  4. Floridi, The 4th Revolution. How the Infosphere is Reshaping Human Reality, Oxford, Oxford University Press, 2014, 156 p.; J. Nieva-fenoll, Inteligencia artificial y proceso judicial, Madrid, Marcial Pons, 2018, 21 p.
  5. Longo, G. Scorza, Intelligenza artificiale: l’impatto sulle nostre vite, diritti e libertà, cit., pp.26-27.
  6. Friedman, H. Nissebaum, Bias in Computer Systems, in “ACM Transactions on Information Systems”, v. 14, n. 3, 1996, 335 p.
  7. University of Helsinki, Reaktor, How Should We Define AI?, in “https://course.elementsofai.com/1/1”.
  8. University of Helsinki, Reaktor, How Should We Define AI?, cit.
  9. Comoglio, Nuova tecnologie e disponibilità Della prova. L’accertamento del fatto nella diffusione delle conoscenze, cit., 335 p.
  10. Joshi, Machine Learning and Artificial Intelligence, cit., 164 p.
  11. Mittelstadt, C. Russell, S. Wachter, Explaining Explanations in AI, in “Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT* ’19)”, 2018, 283 p.
  12. Gunning, M. Stefik, J. Choi, T. Miller, S. Stumpf, G. Yang, Explainable artificial intelligence (XAI), in “DARPA/I20,” 2019, 52 p.
  13. Barredo Arrieta, N. Díaz-Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. Garcia, S. Gil-Lopez, D. Molina, R. Benjamins, R. Chatila, F. Herrera, Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI, in “Information Fusion”, 2020, v. 58, 94 p.
  14. Rai, Explainable AI: from black box to glass box, cit., 138 p.
  15. Samek, A. Binder, G. Montavon, S. Lapuschkin, and K. Müller, Evaluating the visualization of what a deep neural network has learned, in “IEEE Transactions on Neural Networks and Learning Systems”, 2017, v. 28, 2665 p.
  16. Wick, W. Thompson, Reconstructive expert system explanation, in “Artificial Intelligence”, 1992, v. 54, n. 1-2, 58 p.
  17. Rai, Explainable AI: from black box to glass box, cit., 138 p.
  18. Kim, Data-driven discrimination at work, in “Willian & Mary Law Review”, 2017, v. 58, n. 3, 931 p.
  19. Doran, S. Schulz, T. Besold, What does explainable AI really mean? A new conceptualization of perspectives, in “CEUR Workshop Proceedings”, 2018, 2071 p.
  20. Barredo Arrieta, N. Díaz-Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. Garcia, S. Gil-Lopez, D. Molina, R. Benjamins, R. Chatila, F. Herrera, Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI, cit., 101 p.
  21. Roscher, B. Bohn, M. Duarte, J. Garcke, Explainable Machine Learning for Scientific Insights and Discoveries, in “IEEE Access”, 2020, v. 8, 42222 p.
  22. Samek, G. Montavon, S. Lapuschkin, C. Anders, K. Müller, Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications, in “Proceedings of the IEEE”, 2021, v. 109, iss. 3, pp. 247-278.
  23. Murdoch, C. Singh, K. Kumbier, R. Abbasi-Asl, B.Yu, Definitions, methods, and applications in interpretable machine learning, in “Proceedings of the National Academy of Sciences of the United States of America”, 2019, pp. 22079 p.
  24. Samek, G. Montavon, S. Lapuschkin, C. Anders, K. Müller, Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications, cit., pp. 247-278.
  25. Wachter, B. Mittelstadt, C. Russell, Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR, in “Harvard Journal of Law & Technology”, 2018, v. 31, n. 2, 845 p.
  26. Roscher, B. Bohn, M. Duarte, J. Garcke, Explainable Machine Learning for Scientific Insights and Discoveries, cit., 42222. p.
  27. Roscher, B. Bohn, M. Duarte, J. Garcke, Explainable Machine Learning for Scientific Insights and Discoveries, cit., 42222 p.
  28. Wachter, B. Mittelstadt, C. Russell, Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR, cit., 850 p.
  29. Mittelstadt, C. Russell, S. Wachter, Explaining Explanations in AI, cit., 281 p.
  30. Doshi-Velez, M. Kortz, R. Budish, C. Bavitz, S. Gershman, D. O’Brien, K. Scott, J. Waldo, D. Weinberger, A. Weller, A. Wood, A, Accountability of AI Under the Law: The Role of Explanation, cit., 14 p.
  31. Wachter, B. Mittelstadt, C. Russell, Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR, cit., 860 p.
  32. Proposal for a Regulation of the European Parliament and of the Council. Laying Down Harmonized Rules on Artificial Intelligence (Artificial Intelligence Act) and Amending Certain Union Legislative Acts, Brussels, 21.4.2021, COM(2021) 206 final, 2021/0106(COD).
  33. European Commission, A Digital Market Strategy for Europe, COM (2015) 192 final; the next communication touching upon the subject, by the European Commission, was in January 2017, Building a European Data Economy, COM(2017) 9 final; European Commission, Communication from the Commission to the European Parliament, the European Council, the Council, the European Economic and Social Committee and the Committee of the Regions. Artificial Intelligence for Europe, COM(2018) 237 final.
  34. European Economic and Social Committee Opinion. Artificial intelligence – The consequences of artificial intelligence on the (digital) single market, production, consumption, employment and society (own-initiative opinion), (INT/806-EESC-2016-05369-00-00-AC-TRA), in “Official Journal of the European Union”, C 288.; European Economic and Social Committee 526th EESC Plenary Session of 31 May and 1 June 2017, Opinion of the European Economic and Social Committee on ‘Artificial intelligence – The consequences of artificial intelligence on the (digital) single market, production, consumption, employment and society’ (own-initiative opinion) (2017/C 288/01).
  35. European Parliament resolution of 16 February 2017 with recommendations to the Commission on Civil Law Rules on Robotics (2015/2103(INL)).
  36. High-level expert group on artificial intelligence, Ethics Guidelines for Trustworthy AI, Brussels, 2019; U. von der Leyen, A Union that strives for more. My agenda for Europe. Political Guidelines for the Next European Commission 2019-2024; European Commission, White Paper on Artificial Intelligence – A European approach to excellence and trust, COM(2020) 65final.
  37. Annex I, Artificial Intelligence Techniques and Approaches referred to in Article 3, in Annexes to the Proposal for Regulation of the European Parliament and of the Council. Laying Down Harmonized Rules on Artificial Intelligence (Artificial Intelligence Act) and Amending Certain Union Legislative Acts, COM (2021) 206 final, Brussels, 2021.
  38. Annex I, cit., lt. c).
  39. Annex I, cit., lt. b).
  40. Annex I, cit., lt. a)
  41. Proposed AI Act, cit., art. 3(1).
  42. Proposed AI Act, cit., “5.2. Detailed explanation of the specific provisions of the proposal,” point 5.2.1.
  43. Proposed AI Act, cit., recital 6.
  44. Proposed AI Act, cit., art. 4.
  45. Commission Staff Working Document. Executive Summary of the Impact Assessment Report. Accompanying the Proposal for a Regulation of the European Parliament and of the Council. Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) and Amending Certain Union Legislative Acts, SWD(2021) 85 final, Brussels.
  46. Veale, F. Zuiderveen Borgesius, Demystifying the Draft EU Artificial Intelligence Act, in “Computer Law Review International”, v. 22, n. 4, 2021, 100 p.
  47. Decision No 768/2008/EC of the European Parliament and of the Council of 9 July 2008 on a common framework for the marketing of products, and repealing Council Decision 93/465/EEC (OJ L 218, 13.8.2008, p. 82).
  48. [49] Veale, F. Zuiderveen Borgesius, Demystifying the Draft EU Artificial Intelligence Act, cit., 101 p.
  49. Proposed AI Act, cit., art. 16.
  50. Proposed AI Act, cit., art. 6(2).
  51. Annex III, High-Risk AI Systems referred to in Article 6(2), in Annexes to the Proposal for Regulation of the European Parliament and of the Council. Laying Down Harmonized Rules on Artificial Intelligence (Artificial Intelligence Act) and Amending Certain Union Legislative Acts, COM (2021) 206 final, Brussels, 2021.
  52. Proposed AI Act, cit., art. 13.
  53. Proposed AI Act, cit., art. 29(1).
  54. Proposed AI Act, cit., art. 13(1).
  55. Proposed AI Act, cit., art. 13(2).
  56. Proposed AI Act, cit., art. 13(3).
  57. Proposed AI Act, cit., art. 13(3)(iii).
  58. Proposed AI Act, cit., art. 13(3)(v).
  59. See Tribunale di Firenze, Giustizia 4.0. Una metodologia strategia, innovativa e replicabile per risolvere i contenziosi riducendo i tempi della giustizia, in “https://www.forumpachallenge.it/soluzioni/giustizia-semplice-40#”.
  60. Accordo Quadro tra La Corte di Cassazione, Centro Elettronico di Documentazione (C.E.D.), con sede in Roma, presso il Palazzo di Giustizia, piazza Cavour (di seguito anche “C.E.D.”), rappresentata dal Primo Presidente Pietro Curzio e La Scuola Superiore Universitaria Superiore IUSS Pavia, C.F. 96049740184, P.IVA n. 02202080186, con sede in Pavia, presso il Palazzo del Broletto, Piazza della Vittoria n. 15 (di seguito anche “IUSS”), rappresentata dal Rettore Prof. Riccardo Pietrabissa, in “https://www.cortedicassazione.it/cassazione-resources/resources/cms/documents/ACCORDO_TRA_CED_E_SCUOLA_UNIVERSITARIA_SUPERIOR”.
  61. Casaluce, Exploring Trial Courts Legal Databases: Part 2 – Length of legal documents, in “https://www.predictivejurisprudence.eu/exploring-trial-courts-legal-databases-part-2-length-of-legal-documents/”, 2021.
  62. Predictive Justice, in “https://www.predictivejurisprudence.eu/”.
  63. Predictive Justice, Anonymization model, in “https://www.predictivejurisprudence.eu/the_project/anonymization-model/”.
  64. Algorithm Watch, Automating Society Report 2020, Berlin, AlgorithmWatch gGmbH, 2020, 151 p.
  65. Algorithm Watch, Automating Society Report 2020, cit., 151.
  66. Algorithm Watch, Automating Society Report 2020, cit., 151.
  67. Algorithm Watch, Automating Society Report 2020, cit., 151.
  68. Algorithm Watch, Automating Society Report 2020, cit., 151.
  69. Predictive Justice, in “https://www.predictivejurisprudence.eu/”.
  70. Smuha, Feedback from: Legal, Ethical & Accountable Digital Society (LEADS) Lab, University of Birmingham, in “https://ec.europa.eu/info/law/better-regulation/have-your-say/initiatives/12527-Artificial-intelligence-ethical-and-legal-requirements/F2665480_en”, 2021.
  71. Proposta di regolamento, cit., recital 16.
  72. See Amram, High-risk database and the new regulation on AI, SoBigData++ and LeADS joint Awareness Panel, 6.07.2021, ScuolaSantAnna, in “https://www.youtube.com/watch?v=0VDsDBBOkxY&list=WL&index=31”, 2021, 1:53:24 et seq.
  73. See Amram, High-risk database and the new regulation on AI, cit.
  74. Proposed AI Act, cit., art. 7(2)(e).
  75. Proposed AI Act, cit., art. 2.
  76. See Licari, Predictive Justice. Towards a fully automated data-driven platform for Legal Analytics, cit.
  77. Akoma Ntoso, in “http://www.akomantoso.org/”.
  78. “For example, each sentence is evaluated by 3 experts and we apply a majority vote for the choice of the final label. We avoid the labelling by creating a web application for annotations so the expert can easily choose the right label”. See. D. Licari, Predictive Justice. Towards a fully automated data-driven platform for Legal Analytics, cit.
  79. See Licari, Predictive Justice. Towards a fully automated data-driven platform for Legal Analytics, SoBigData++ and LeADS joint Awareness Panel, 6.07.2021, ScuolaSantAnna, 2021, 1:35:36 et seq, https://www.youtube.com/watch?v=0VDsDBBOkxY&list=WL&index=31
  80. Doshi-Velez, M. Kortz, Accountability of AI Under the Law: The Role of Explanation. Berkman Klein Center Working Group on AI Interpretability, in Berkman Klein Center for Internet & Society working paper, 2017.
  81. Remember “unconditional counterfactual explanation” refer to the minimum conditions that would have led to a different result. see Wachter, B. Mittelstadt, C. Russell, Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR, in Harvard Journal of Law & Technology, vol. 31, no. 2, 2018, 841–887.
Regular Issue Subscription Review Article
Volume 11
Issue 01
Received March 1, 2024
Accepted March 27, 2024
Published April 24, 2024
Views: 24