Author
M. Uma Shankari, S.Hareesh
Keywords
Natural Language Processing; Machine Translation; Potential Ethical Issues.
Abstract
The development of natural language processing (NLP) and machine translation (MT) systems necessitates careful consideration of their ethical and societal implications. The implementation of these technologies can have significant consequences for individuals, communities, and society as a whole. Therefore, it is imperative to conduct extensive research and analysis to identify potential ethical issues and formulate guidelines and best practices to address them. It is also essential to engage with stakeholders, including users, communities, and experts, to ensure that their perspectives and concerns are taken into account. By considering the ethical and societal implications of NLP and MT systems, we can ensure that these technologies are developed and deployed in a responsible manner that benefits society as a whole.
References
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[4] Obermeyer, Z.; Powers, B.; Vogeli, C.; Mullainathan, S. Dissecting racial bias in an algorithm used to manage the health of populations. Science 2019,366, 447–453.
[5] Rodger, J.A.; Pendharkar, P.C. A field study of the impact of gender and user’s technical experience on the performance of voice-activated medical tracking application. Int. J. Hum. Compute. Stud. 2004,60, 529–544.
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[7] Jin, Z., & Mihalcea, R. (2022). Natural language processing for policymaking. In E. Bertoni, M. Fontana, L. Gabrielli, S. Signorelli, & M. Vespe (Eds.), Handbook of computational social science for policy (pp. 141-162). Springer.
[8] Garrido-Muñoz, I., Montejo-Ráez, A., Martínez-Santiago, F., & Ureña-López, L. A. (2021). A survey on bias in deep NLP. Applied Sciences, 11(7), Article 3184.
[9] Hovy, D., & Prabhumoye, S. (2021). Five sources of bias in natural language processing. Language and Linguistics Compass, 15(8), Article e12432. https://doi.org/https://doi.org/10.1111/lnc3.12432
[10] Königs, P. (2022). Artificial intelligence and responsibility gaps: What is the problem? Ethics and Information Technology, 24(3), Article 36. https://doi.org/10.1007/s10676-022-09643-0
[11] Kreutzer, T., Vinck, P., Pham, P. N., An, A., Appel, L., DeLuca, E., Tang, G., Alzghool, M., Hachhethu, K., Morris, B., Walton-Ellery, S. L., Crowley, J., & Orbinski, J. (2020). Improving humanitarian needs assessments through natural language processing. IBM Journal of Research and Development, 64(1/2), 9:1-9:14. https://doi.org/10.1147/JRD.2019.2947014
[12] Liu, X., Xie, L., Wang, Y., Zou, J., Xiong, J., Ying, Z., & Vasilakos, A. V. (2021). Privacy and security issues in deep learning: A survey. IEEE Access, 9, 4566-4593. https://doi.org/ 10.1109/ACCESS.2020.3045078 Stanczak, K., & Augenstein, I. (2021). A survey on gender bias in natural language processing. arXiv preprint, arXiv:2112.14168.
[13] Tang, C. (2022). Privacy protection dilemma and improved algorithm construction based on deep learning in the era of artificial intelligence. Security and Communication Networks, 2022, Article 8711962. https://doi.org/10.1155/2022/8711962
[14] Xiao, Y., & Wang, W. Y. (2019). Quantifying uncertainties in natural language processing tasks. Proceedings of the AAAI conference on artificial intelligence, 33(1), 7322-7329. https://doi.org/10.1609/aaai.v33i01.33017322
Received : 18 February 2025
Accepted : 22 May 2025
Published : 25 May 2025
DOI: 10.30726/esij/v12.i2.2025.122003