Open Access
Issue
ITM Web Conf.
Volume 38, 2021
International Conference on Exploring Service Science (IESS 2.1)
Article Number 02007
Number of page(s) 13
Section Conference Papers
DOI https://doi.org/10.1051/itmconf/20213802007
Published online 07 May 2021
  1. T. Skjolsvik and J. K. Breunig. Virtual Law Firms: An Exploration of the Media Coverage of an Emerging Archetype. Int. J. of Law and Tech. 26(1), 64–88 (2018) [Google Scholar]
  2. J. McGinnis and R. Pearce. The Great Disruption: How Machine Intelligence Will Transform the Role of Lawyers in the Delivery of Legal Services. Fordham Law Review 82(6), 3014–3065 (2014) [Google Scholar]
  3. R. Susskind. The End of Lawyers? Rethinking the Nature of Legal Services. (Oxford University Press, 2010) [Google Scholar]
  4. R. Wang. Legal technology in contemporary USA and China. Comput. Law and Sec. Review 39, 105459 (2020) [Google Scholar]
  5. K. Yeung. Are Human Biomedical Interventions Legitimate Regulatory Policy Instruments? The Oxford Handbook of Law, Regulation and Technology. (Oxford University Press, 2017) [Google Scholar]
  6. F. Ryan. Rage against the machine? Incorporating legal tech into legal education. Law Teach. online (2020) [Google Scholar]
  7. M. Smith. Integrating technology in contemporary legal education. Law Teach. 54(2), 209–221 (2020) [Google Scholar]
  8. C. Strevens, C. Welch, and R. Welch. On-line legal services and the changing legal market: Preparing law undergraduates for the future. Law Teach. 45(3), 328–347 (2011) [Google Scholar]
  9. D. Jackson. Human-Centered legal tech: Integrating design in legal education. Law Teach. 50(1), 82–97 (2016) [Google Scholar]
  10. P. P. Maglio and J. Spohrer. Fundamentals of service science. J. Acad. Mark. Sci. 36(1), 18–20 (2008) [Google Scholar]
  11. M. Hartung, M.-M. Bues, and G. Halbleib. Legal tech. (CH Beck, 2017) [Google Scholar]
  12. K. Galloway et al. The Legal Academy’s Engagements with Lawtech: Technology Narratives and Archetypes as Drivers of Change. Law, Tech. & Hum. 1, 27 (2019) [Google Scholar]
  13. V. Janecek, R. Williams, and E. Keep. Education for the provision of technologically enhanced legal services. Comput. Law and Sec. Review 40, 105519 (2021) [Google Scholar]
  14. M. Sako et al. Scaling Up Firms in Entrepreneurial Ecosystems: Fintech and Lawtech Ecosystems Compared. Available at SSRN 3520533 (2020) [Google Scholar]
  15. J. Spohrer and S. K. Kwan. Service Science, Management, Engineering, and Design (SSMED): An Emerging Discipline - Outline & References. Int. J. of Inf. Syst. in the Serv. Sect. (IJISSS) 1(3), 1–31 (2009). [Google Scholar]
  16. J. Spohrer, P.P. Maglio, Toward a Science of Service Systems, in Handbook of Service Science, P.P. Maglio, C.A. Kieliszewski, J.C. Spohrer (Eds.) (Springer US, Boston, MA, 2010) 157–194 [Google Scholar]
  17. L. Carrubbo, M. Dragoicea, X. Hisa, A. Megaro, B. Zenelay, Is there a relationship of interdependence between resilience, viability and competitiveness? Ditron Ltd. case-study, in Proceedings of International Conference on Exploring Service Science, IESS2020, LNBIP 377, 363–376 (2020) [Google Scholar]
  18. M. Dragoicea, N. G. Badr, J. Falcao e Cunha, V. E. Oltean. From Data to Service Intelligence: Exploring Public Safety as a Service. In: Proceedings of International Conference on Exploring Service Science, IESS2018, LNBIP 331, 344–357 (Springer, 2018) [Google Scholar]
  19. H. Zillmer. Justice as a Service. https://henrikzillmer.com/justice-as-a-service (2016) [Google Scholar]
  20. The UK Law Society. Introduction to LawTech. Available at www.lawsociety.org.uk (2019) [Google Scholar]
  21. The UK Law Society. LawTech Adoption Research. Available at www.lawsociety.org.uk (2019) [Google Scholar]
  22. D. W. Oard and W. Webber. Information retrieval for e-discovery. Inf. Retr. J. 7(2-3), 99–237 (2013) [Google Scholar]
  23. M. Dragoicea, L. Bucur, and M. Patragcu, A service oriented simulation architecture for intelligent building management. In: Proceedings of International Conference on Exploring Service Science, IESS2013, LNBIP 143, 14–28 (Springer, 2013). [Google Scholar]
  24. B. Alarie, A. Niblett, and A. Yoon. Law in the Future. Univ Tor Law J 66(4), 423–428 (2016) [Google Scholar]
  25. A. Casey and A. Niblett. Self-Driving Laws. Univ Tor Law J 66(4), 429–442 (2016) [Google Scholar]
  26. R. Susskind and D. Susskind. The Future of Professions. (Oxford University Press, 2015) [Google Scholar]
  27. R. Susskind. Online Courts and the Future ofJustice. (Oxford University Press, 2019) [Google Scholar]
  28. P. Gowder. Transformative Legal Technology and the Rule of Law. Univ Tor Law J 68(82) 1–16 (2018) [Google Scholar]
  29. E. De of City. Janus-Faced Justice? The Role of Legal Technology in the Provision of Access to Justice. Legal Inf Manage 19, 63–65 (2019) [Google Scholar]
  30. S. Jasanoff. Just evidence: The Limits of Science in the Legal Process. J. of Law, Med. and Ethics 34, 328–341 (2006) [Google Scholar]
  31. C. B. Graber. Internet Creativity, Communicative Freedom and a Constitutional Rights Theory Response to ‘Code is Law’. Transnational Culture in the Internet Age. (Edward Elgar, 2012) [Google Scholar]
  32. M. Hildebrandt. Law as computation in the era of artificial intelligence: Speaking to the power of statistics. Univ Tor Law J 68(1), 12–35 (2018) [Google Scholar]
  33. J. R. Reidenberg. Lex Informatica: The Formulation of Information Policy Rules through Technology. Texas Law Review 76(3), 552–593 (1997) [Google Scholar]
  34. N. Luhmann. Law as a Social System. (Oxford University Press, 2008) [Google Scholar]
  35. F. Pasquale. The Black Box Society. (Harvard University Press, 2015) [Google Scholar]
  36. M. Hildebrandt. Law as Information in the Era of Data Driven Agency. Modern Law Review 79(1), 1–30 (2016) [Google Scholar]
  37. R. Susskind. Expert Systems in Law. (Oxford University Press, 1987) [Google Scholar]
  38. J. Zeleznikow. Building Decision Support Systems in Discretionary Legal Domains. Int. Review of Law, Comp. & Tech. 14(3), 341–356 (2000) [Google Scholar]
  39. C. O’Neil. Weapons ofMath Destruction. How Big Data increases Inequality and Threatens Democracy. (Crown Publishing, 2016) [Google Scholar]
  40. H. Katzan Jr. On an ontological view of cloud computing. J. of Serv. Sci. 3(1), 1–6 (2010). [Google Scholar]
  41. S. Seebacher and R. Schuritz. Blockchain Technology as an Enabler of Service Systems: A Structured Literature Review. In: Proceedings ofInternational Conference on Exploring Service Science, IESS2017, LNBIP 279, 12–23, (Springer, 2017) [Google Scholar]
  42. P. Ferreira, J. G. Teixeira, and L. F. Teixeira. Understanding the Impact of Artificial Intelligence on Services. In: Proceedings of International Conference on Exploring Service Science, IESS2020, LNBIP 377, 202–213, (Springer, 2020) [Google Scholar]
  43. J. Reis, P. E. Santo, and N. Melao. Artificial Intelligence Theory in Service Management. In: Proceedings of International Conference on Exploring Service Science, IESS2020, LNBIP 377, 137–149, (Springer, 2020) [Google Scholar]
  44. R. Dale. Law and Word Order: NLP in Legal Tech. Nat. Lang. Eng. 25(1), 211–217, (Cambridge University Press, 2019) [Google Scholar]
  45. D. Yaga, P. Mell, N. Roby, K. Scarfone. “Blockchain technology overview”. ArXiv preprint 1906.11078, https://arxiv.org/abs/1906.11078 (2019) [Google Scholar]
  46. C. Dannen. Introducing Ethereum and solidity. (Springer, 2017) [Google Scholar]
  47. J. Chen and S. Micali. Algorand: A secure and efficient distributed ledger. Theor Comput Sci 777, 155–183, (2019) [Google Scholar]
  48. D. Drummer and D. Neumann. Is code law? Current legal and technical adoption issues and remedies for blockchain-enabled smart contracts. J. Inf. Technol. Impact 35(4), 337–360, (2020) [Google Scholar]
  49. A. Vaswani et al. Attention is All you Need. In: Proc ofAnnual Conference on Neural Information Processing Systems 2017, 5998–6008, (2017) [Google Scholar]
  50. A. Radford et al. Improving language understanding by generative pre-training. https://www.cs.ubc.ca/~amuham01/LING530/papers/radford2018improving.pdf (Preprint, 2018) [Google Scholar]
  51. J. Devlin et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In: Proceedings of NAACL-HLT, 4171–4186, (2019) [Google Scholar]
  52. A. Radford et al. Language models are unsupervised multitask learners. Technical Report, OpenAI (2019) [Google Scholar]
  53. M. Shoeybi et al. Megatron-lm: Training multi-billion parameter language models using gpu model parallelism. ArXiv preprint 1909.08053, (2019) [Google Scholar]
  54. Microsoft Inc. Turing-NLG: A 17-billion-parameter language model by Microsoft. Microsoft Research Report, (2020). [Google Scholar]
  55. T. B. Brown et al. “Language models are few-shot learners”. ArXiv preprint 2005.14165, https://arxiv.org/abs/2005.14165 (2020) [Google Scholar]
  56. R. Dale. GPT-3: What’s it good for? Nat. Lang. Eng. 27(1), 113–118, (Cambridge University Press, 2021) [Google Scholar]

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