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Advancing AI with Integrity: Ethical Challenges and Solutions in Neural Machine Translation

Published 1 Apr 2024 in cs.CL and cs.AI | (2404.01070v1)

Abstract: This paper addresses the ethical challenges of Artificial Intelligence in Neural Machine Translation (NMT) systems, emphasizing the imperative for developers to ensure fairness and cultural sensitivity. We investigate the ethical competence of AI models in NMT, examining the Ethical considerations at each stage of NMT development, including data handling, privacy, data ownership, and consent. We identify and address ethical issues through empirical studies. These include employing Transformer models for Luganda-English translations and enhancing efficiency with sentence mini-batching. And complementary studies that refine data labeling techniques and fine-tune BERT and Longformer models for analyzing Luganda and English social media content. Our second approach is a literature review from databases such as Google Scholar and platforms like GitHub. Additionally, the paper probes the distribution of responsibility between AI systems and humans, underscoring the essential role of human oversight in upholding NMT ethical standards. Incorporating a biblical perspective, we discuss the societal impact of NMT and the broader ethical responsibilities of developers, positing them as stewards accountable for the societal repercussions of their creations.

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References (51)
  1. M. Burget, E. Bardone, and M. Pedaste, “Definitions and conceptual dimensions of responsible research and innovation: A literature review,” Science and engineering ethics, vol. 23, pp. 1–19, 2017.
  2. A. Josefová, “The importance of ethics in the process of education in today globalised society,” in SHS Web of Conferences, vol. 26, p. 01019, EDP Sciences, 2016.
  3. J. Natyavidushi, “Importance of body language in effective multicultural communication.,” Annals of the University of Craiova. Series Geography/Analele Universitatii din Craiova. Seria Geografie, vol. 14, 2011.
  4. R. H. McCuen, “Engineering ethics in a multicultural global economy,” Journal of professional issues in engineering education and practice, vol. 117, no. 3, pp. 258–266, 1991.
  5. M. Fenwick, M. C. Compagnucci, N. Forgo, and T. Barninghausen, “Mapping the digital healthcare revolution,” in AI in EHealth, pp. 1–16, Cambridge University Press, 2022.
  6. F. Caccavale, C. L. Gargalo, K. V. Gernaey, and U. Krühne, “To be fair: ethical and fair application of artificial intelligence in virtual laboratories,” Towards a new future in engineering education, new scenarios that european alliances of tech universities open up, 2022.
  7. L. Mirghaderi, M. Sziron, and E. Hildt, “Ethics and transparency issues in digital platforms: An overview,” AI, 2023.
  8. J. Bandy, “Inspecting algorithmic flows: Ethics, transparency, and accountability for digital mass communication platforms,” Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, 2022.
  9. D. C. Hill, C. Gombay, O. Sanchez, B. Woappi, A. S. Romero Vélez, S. Davidson, and E. Z. Richardson, “Lost in machine translation: The promises and pitfalls of machine translation for multilingual group work in global health education,” Discover Education, vol. 1, no. 1, p. 3, 2022.
  10. J. Moorkens, “Ethics and machine translation,” Machine translation for everyone: Empowering users in the age of artificial intelligence, vol. 18, p. 121, 2022.
  11. K. Liu, H. L. Kwok, J. Liu, and A. K. Cheung, “Sustainability and influence of machine translation: Perceptions and attitudes of translation instructors and learners in hong kong,” Sustainability, 2022.
  12. M. Mager, E. Mager, K. Kann, and N. T. Vu, “Ethical considerations for machine translation of indigenous languages: Giving a voice to the speakers,” arXiv preprint arXiv:2305.19474, 2023.
  13. L. Haroutunian, “Ethical considerations for low-resourced machine translation,” in Annual Meeting of the Association for Computational Linguistics, 2022.
  14. A. Jain, A. Banerjee, and P. Bhattacharyya, “Literature survey: Neural machine translation in low resource setting,” 2021.
  15. S. M. U. Qumar, M. Azim, and S. M. K. Quadri, “Neural machine translation: A survey of methods used for low resource languages,” 2023 10th International Conference on Computing for Sustainable Global Development (INDIACom), pp. 1640–1647, 2023.
  16. M. Bailey, D. Dittrich, E. Kenneally, and D. Maughan, “The menlo report,” IEEE Security & Privacy, vol. 10, no. 2, pp. 71–75, 2012.
  17. T. Winkler and S. Spiekermann, “Twenty years of value sensitive design: a review of methodological practices in vsd projects,” Ethics and Information Technology, vol. 23, pp. 17–21, 2021.
  18. S. Umbrello and I. Van de Poel, “Mapping value sensitive design onto ai for social good principles,” AI and Ethics, vol. 1, no. 3, pp. 283–296, 2021.
  19. A. F. T. Winfield and K. Winkle, “Roboted: a case study in ethical risk assessment,” 2020.
  20. M. Pansera, R. Owen, D. Meacham, and V. Kuh, “Embedding responsible innovation within synthetic biology research and innovation: insights from a uk multi-disciplinary research centre,” Journal of Responsible Innovation, vol. 7, pp. 384–409, 2020.
  21. W. Reijers and P. Brey, “A common framework for ethical impact assessment,” 2017.
  22. A. Ema, “Design dialogue and community to discuss ieee “ethically aligned design”,” 2018.
  23. E. Kazim and A. S. Koshiyama, “Human centric ai: A comment on the ieee’s ethically aligned design,” Artificial Intelligence - Law, 2020.
  24. N. Meade, E. Poole-Dayan, and S. Reddy, “An empirical survey of the effectiveness of debiasing techniques for pre-trained language models,” 2022.
  25. R. Kimera, D. N. Rim, and H. Choi, “Building a parallel corpus and training translation models between luganda and english,” arXiv preprint arXiv:2301.02773, 2023.
  26. D. N. Rim, R. Kimera, and H. Choi, “Mini-batching with similar-length sentences to quickly train nmt models,” Journal of Information Science, vol. 50, no. 7, pp. 614–620, 2023.
  27. R. Kimera, D. N. Rim, J. Kirabira, U. G. Udomah, and H. Choi, “Enhanced labeling technique for reddit text and fine-tuned longformer models for classifying depression severity in english and luganda,” in 2023 14th International Conference on Information and Communication Technology Convergence (ICTC), pp. 76–80, IEEE, 2023.
  28. R. Kimera, D. N. Rim, and H. Choi, “Fine-tuning bert on twitter and reddit data in luganda and english,” in Proceedings of the 2023 7th International Conference on Natural Language Processing and Information Retrieval, pp. 63–70, 2023.
  29. G. Jackson-Koku, “Beck depression inventory,” Occupational Medicine, vol. 66, no. 2, pp. 174–175, 2016.
  30. A. E. Johnson, L. Bulgarelli, L. Shen, A. Gayles, A. Shammout, S. Horng, T. J. Pollard, S. Hao, B. Moody, B. Gow, et al., “Mimic-iv, a freely accessible electronic health record dataset,” Scientific data, vol. 10, no. 1, p. 1, 2023.
  31. M. Mahamud, Z. Lee, and I. Samsten, “Distributional data augmentation methods for low resource language,” arXiv preprint arXiv:2309.04862, 2023.
  32. M. Dehouck and C. Gómez-Rodríguez, “Data augmentation via subtree swapping for dependency parsing of low-resource languages,” in 28th International Conference on Computational Linguistics, pp. 3818–3830, International Committee on Computational Linguistics; International …, 2020.
  33. H. Huang, S. Wu, K. Chen, H. Di, M. Yang, and T. Zhao, “Improving translation quality estimation with bias mitigation,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2175–2190, 2023.
  34. G. Gurov, “Learning neural machine translation tools as a component of training professional translators,” 2022.
  35. I. Rubinic, M. Kurtov, I. Rubinic, R. Likic, P. I. Dargan, and D. M. Wood, “Artificial intelligence in clinical pharmacology: a case study and scoping review of large language models and bioweapon potential,” British Journal of Clinical Pharmacology, 2023.
  36. C. Kooli and H. A. Muftah, “Artificial intelligence in healthcare: a comprehensive review of its ethical concerns,” Technological Sustainability, 2022.
  37. J. Czestochowska, K. Gligoric, M. Peyrard, Y. Mentha, M. Bien, A. Grutter, A. Auer, A. Xanthos, and R. West, “On the context-free ambiguity of emoji,” 2022.
  38. F. Gholami, Z. Rahmati, A. Mofidi, and M. Abbaszadeh, “On enhancement of text classification and analysis of text emotions using graph machine learning and ensemble learning methods on non-english datasets,” Algorithms, 2023.
  39. S. Chauhan and P. Daniel, “A comprehensive survey on various fully automatic machine translation evaluation metrics,” Neural Processing Letters, pp. 1–55, 2022.
  40. D. G. Widder, D. Nafus, L. Dabbish, and J. Herbsleb, “Limits and possibilities for “ethical ai” in open source: A study of deepfakes,” in Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, pp. 2035–2046, 2022.
  41. T. Lysaght, A. Ballantyne, V. Xafis, S. Ong, G. O. Schaefer, J. M. T. Ling, A. J. Newson, I. W. Khor, and E. S. Tai, ““who is watching the watchdog?”: ethical perspectives of sharing health-related data for precision medicine in singapore,” BMC Medical Ethics, vol. 21, no. 1, pp. 1–11, 2020.
  42. S. Zasiekin and S. Vakuliuk, “Ethical issues of neural machine translation,” Psycholinguistics in a Modern World, vol. 15, pp. 81–83, 2020.
  43. I. Provilkov, D. Emelianenko, and E. Voita, “Bpe-dropout: Simple and effective subword regularization,” 2020.
  44. C. L. Cole, S. Sengupta, S. Rossetti, D. K. Vawdrey, M. Halaas, T. M. Maddox, G. Gordon, T. Dave, P. R. Payne, A. E. Williams, et al., “Ten principles for data sharing and commercialization,” Journal of the American Medical Informatics Association, vol. 28, no. 3, pp. 646–649, 2021.
  45. T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” Advances in neural information processing systems, vol. 26, 2013.
  46. S. Krügel, A. Ostermaier, and M. Uhl, “The moral authority of chatgpt,” arXiv preprint arXiv:2301.07098, 2023.
  47. K. A. Folse, “Ethics and the profession: Graduate student training,” Teaching Sociology, pp. 344–350, 1991.
  48. L. R. Churchill, “The teaching of ethics and moral values in teaching: Some contemporary confusions,” The journal of higher education, vol. 53, no. 3, pp. 296–306, 1982.
  49. E. Jefferson, C. Cole, A. C. Boixader, S. Rogers, M. Malone, F. Ritchie, J. Smith, F. Tava, A. Daly, J. Beggs, et al., “Graimatter: Guidelines and resources for ai model access from trusted research environments (graimatter).,” International Journal of Population Data Science, vol. 7, no. 3, 2022.
  50. R. Gianni, S. Lehtinen, and M. Nieminen, “Governance of responsible ai: From ethical guidelines to cooperative policies,” Frontiers in Computer Science, vol. 4, p. 873437, 2022.
  51. M. Balas, J. J. Wadden, P. C. Hébert, E. Mathison, M. D. Warren, V. Seavilleklein, D. Wyzynski, A. Callahan, S. A. Crawford, P. Arjmand, et al., “Exploring the potential utility of ai large language models for medical ethics: an expert panel evaluation of gpt-4,” Journal of Medical Ethics, 2023.
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