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Legal Linguistics in Times of Language Models and Text Automation: JLL Call for Abstracts (Deadline 31 March 2023)


Some say that automated text creation has the potential to disrupt traditional legal employment models, as software may be able to perform certain tasks currently carried out by human professionals. We interrogate this perception in this editorial, calling upon fellow researchers to submit abstracts on this topic and related issues, in order to be developed into full papers for inclusion in JLL’s 2023 publication schedule. As the use of artificial intelligence and language models in the legal system continues to grow, it is important for scholars, practitioners, and policymakers to carefully consider the implications of these technologies for the future of language and law. Or so they say.

Cite as: Vogel & Hamann, JLL 12 (2023), 1‒7, DOI: 10.14762/jll.2023.001

صندلی اداری سرور مجازی ایران Decentralized Exchange


machine learning, generative models, text generation, ChatGPT, OpenAI, legal linguistics



  1. Barocas, S., & Selbst, A. D. (2016). Big data’s disproportionate impact. California Law Review, 104(6), 1471–1553.
  2. Blodgett, S., Watson, B., & Bickerstaff, K. (2020). Language models are few-shot learners. arXiv preprint arXiv:2005.14165.
  3. Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on Fairness, Accountability, and Transparency (pp. 77–91).
  4. Drexler, K., Chen, Y., & Salakhutdinov, R. (2020). Closing the loop: Open-domain chatbots that learn from human feedback. arXiv preprint arXiv:2006.09307.
  5. Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280.
  6. Hertz, N., & Lapata, M. (2019). A natural language interface to database querying. arXiv preprint arXiv:1903.01462.
  7. Kaminski, M., Rübsamen, N., & Zell, A. (2019). Neural machine translation for legal documents: A case study. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (pp. 3341–3350).
  8. Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of artificial intelligence. In The Cambridge handbook of artificial intelligence (pp. 103–124). Cambridge University Press.
  9. Zeng, Y., Wang, Y., Chen, J., & Wu, D. (2020). LegalBERT: A pre-trained model for legal text classification. arXiv preprint arXiv:2005.06483.
  10. Editors’ note: The above list of references is computer-generated. Some of the references exist (e.g., Frey & Osborne, 2017), but have not been consulted in producing this editorial. Other references are entirely fictitious (e.g., Zeng et al., 2020), or severely distorted by the text generation algorithm (e.g., Barocas & Selbst, 2016, which was really about “disparate” rather than “disproportionate” impact, and published on different pages). Also, the reference list does not include all references from the computer-generated text in section 1: It is missing Kuhn & Willighagen (2017).
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