Outline of an Artificial Intelligence Literacy Framework for Translation, Interpreting and Specialised Communication

Ralph Krüger

Abstract


This paper first traces the AI-induced automation of the digitalised and datafied language industry, with a focus on neural machine translation and large language models. Then, it discusses a range of digital literacies that have become increasingly relevant in the language industry in light of these technologies, i.e., machine translation literacy, data literacy and artificial intelligence literacy. After highlighting the interface between these three literacies, the paper sketches an outline of an artificial intelligence literacy framework for translation, interpreting and specialised communication. This framework intends to capture an extensive set of competences required by stakeholders in the AI-saturated language industry.

Keywords


language industry; artificial intelligence; neural machine translation; large language models; machine translation literacy; data literacy; artificial intelligence literacy

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References


Alemohammad, S., Casco-Rodriguez, J., Luzi, L., Humayun, A. I., Babaei, H., LeJeune, D., Siahkoohi, A., & Baraniuk, R. G. (2023). Self-consuming generative models go MAD. arXiv. https://doi.org/10.48550/arXiv.2307.01850

Bianchi, F., Fornaciari, T., Hovy, D., & Nozza, D. (2023). Gender and age bias in commercial machine translation. In H. Moniz, & C. Parra Escartín (Eds.), Towards responsible machine translation. Ethical and legal considerations in machine translation (pp. 1–11). Springer. https://doi.org/10.1007/978-3-031-14689-3_9

Brey, P. A. E. (2012). Anticipatory ethics for emerging technologies. NanoEthics, 6, 1–13. http://doi.org/10.1007/s11569-012-0141-7

Crawford, K. (2021). Atlas of AI. Power, politics, and the planetary costs of artificial intelligence. Yale University Press.

DataLitMT (2023). DataLitMT project website. https://itmk.github.io/The-DataLitMT-Project/

Dong, Q., Li, L., Dai, D., Zheng, C., Wu, Z., Chang, B., Sun, X., Xu, J, Li, L., & Sui, Z. (2023). A survey on in-context learning. arXiv. https://doi.org/10.48550/arXiv.2301.00234

Ehrensberger-Dow, M., & Massey, G. (2017). Socio-technical issues in professional translation practice. Translation Spaces, 6(1), 104–121. https://doi.org/10.1075/ts.6.1.06ehr

ELIS Research (2023). European language industry survey 2023. https://elis-survey.org/

European Parliament (2023). EU AI Act: first regulation on artificial intelligence. h t t p s : / / w w w. e u r o p a r l . e u r o p a . e u / t o p i c s / e n / a r t i c l e / 2 0 2 3 0 6 0 1 S T O 9 3 8 0 4 /eu-ai-act-first-regulation-on-artificial-intelligence

European Parliamentary Research Service (2023). General-purpose artificial intelligence. European Parliament. https://www.europarl.europa.eu/RegData/etudes/ATAG/2023/745708/EPRS_ATA(2023)745708_EN.pdf

Gu, A., & Dao, T. (2023). Mamba: Linear time-sequence modelling with selective state spaces. arXiv. https://doi.org/10.48550/arXiv.2312.00752

Herbig, N., Pal, S., van Genabith, J., & Krüger, A. (2019a). Multi-modal approaches for post-editing machine translation. In S. Brewster, G. Fitzpatrick, A. Cox, & V. Kostakos (Eds.), CHI ’19: Proceedings of the 2019 CHI conference on human factors in computing systems (pp. 1–11).

Association for Computing Machinery. https://doi.org/10.1145/3290605.3300461

Krüger, R. (2022). Integrating professional machine translation literacy and data literacy. Lebende Sprachen, 67(2), 247–282. https://doi.org/10.1515/les-2022-1022

Krüger, R. (2023). Artificial intelligence literacy for the language industry – with particular emphasis on recent large language models such as GPT-4. Lebende Sprachen, 68(2), 283–330. https://doi.org/10.1515/les-2023-0024

Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In R. Bernhaupt, F. Mueller, D. Verweij, & J. Andres (Eds.), CHI ‘20: Proceedings of the 2020 CHI conference on human factors in computing systems (pp. 1–16). Association for Computing Machinery. https://dl.acm.org/doi/10.1145/3313831.3376727

Markauskaite, L., Marrone, R., Poquet, O., Knight, S., Martinez-Maldonado, R., Howard, S. Tondeur, J., De Laat, M., Buckingham Shum, S., Gasevic, D., & Siemens, G. (2022). Rethinking the entwinement between artificial intelligence and human learning: What capabilities do learners need for a world with AI? Computers and Education: Artificial Intelligence, 3, 1–16. https://doi.org/10.1016/j.caeai.2022.100056

Moniz, H., & Parra Escartín, C. (2023) (Eds.). Towards responsible machine translation. Ethical and legal considerations in machine translation. Springer. https://doi.org/10.1007/978-3-031-14689-3

Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 1–11. https://doi.org/10.1016/j.caeai.2021.100041

O’Brien, S., & Ehrensberger-Dow, M. (2020). MT literacy – a cognitive view. Translation, Cognition & Behaviour, 3(2), 145–164. https://doi.org/10.1075/tcb.00038.obr

Ridsdale, C., Rothwell, J., Smit, M., Ali-Hassan, H., Bliemel, M., Irvine, D., Kelley, D., Matwin, S., & Wuetherick, B. (2015). Strategies and best practices for data literacy education. Knowledge synthesis report. Dalhousie University. http://hdl.handle.net/10222/64578

Sakamoto, A. (2019). Why do many translators resist post-editing? A sociological analysis using Bourdieu’s concepts. Journal of Specialised Translation, 31, 201–216. https://jostrans.soap2.ch/issue31/art_sakamoto.php

Salesforce (2023). More than half of generative AI adopters use unapproved tools at work. https://www.salesforce.com/news/stories/ai-at-work-research/

Schüller, K., Rampelt, F., Koch, H., & Schleiss, J. (2023). Better ready than just aware: Data and AI Literacy as an enabler for informed decision making in the data age. In M. Klein, D. Krupka, C. Winter, & V. Wohlgemuth (Eds.), INFORMATIK 2023, Lecture Notes in Informatics (LNI) (pp. 425–430). Gesellschaft für Informatik. https://doi.org/10.18420/inf2023_49

Shumailov, I., Shumaylov, Z., Zhao, Y., Gal, Y., Papernot, N., & Anderson, R. (2023). The curse of recursion: Training on generated data makes models forget. arXiv. https://doi.org/10.48550/arXiv.2305.17493

Szczerbicki, E., & Nguyen, N. T. (2021). Intelligence augmentation and amplification: Approaches, tools, and case studies. Cybernetics and Systems, 53(5), 381–383. https://doi.org/10.1080/01969722.2021.2018551

Van Lier, M. (2023). Understanding large language models through the lens of artificial agency. In H. Grahn, A. Borg, & M. Boldt (Eds.), 35th annual workshop of the Swedish Artificial Intelligence Society (SAIS 2023) (pp. 79–84). https://doi.org/10.3384/ecp199008

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, Ł. (2017). Attention is all you need. In I. Guyon, U. von Luxburg, S. Bengio, H. M. Wallach, R. Fergus, S. V. N. Vishwanathan, & R. Garnett (Eds.), Advances in neural information processing systems 30 (NIPS 2017) (pp. 1–11). https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html

Yong, Z.-X., Menghini, C., & Bach, S. H. (2024). Low-resource languages jailbreak GPT-4. arXiv. https://doi.org/10.48550/arXiv.2310.02446




DOI: http://dx.doi.org/10.17951/lsmll.2024.48.3.11-23
Date of publication: 2024-10-07 11:52:21
Date of submission: 2024-03-17 17:02:39


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