Open Access
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
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. (2016) [Google Scholar]
  20. The UK Law Society. Introduction to LawTech. Available at (2019) [Google Scholar]
  21. The UK Law Society. LawTech Adoption Research. Available at (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, (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. (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, (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]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.