Ethical Challenges in Natural Language Processing: A Comparative Study of Solutions Across Multiple Domains

Year : 2026 | Volume : 13 | Issue : 01 | Page : 01 07
    By

    Khushi Singh,

  • Sheetal Singh,

  • Aritro Chakraborty,

  • Himanshu Singh,

  • Sujeet Kumar,

  1. Student, Department of Computer Application, Echelon Institute of Technology, Faridabad, Haryana, India
  2. Student, Department of Computer Application, Echelon Institute of Technology, Faridabad, Haryana, India
  3. Student, Department of Computer Application, Echelon Institute of Technology, Faridabad, Haryana, India
  4. Student, Department of Computer Application, Echelon Institute of Technology, Faridabad, Haryana, India
  5. Student, Department of Computer Application, GL BAJAJ Institute of Management, Greater Noida, Uttar Pradesh, India

Abstract

This comparative analysis investigates the ethical challenges associated with natural language processing (NLP) by reviewing and synthesizing insights from ten influential and widely cited publications in the field. As NLP technologies are increasingly integrated into domains such as healthcare, finance, education, and governance, ethical concerns related to algorithmic bias, data privacy, fairness, accountability, and system transparency have become more prominent. This paper systematically examines how different researchers conceptualize and address these ethical issues, highlighting both converging and diverging perspectives. Particular attention is given to contrasting approaches to data privacy, including consent, anonymization, and responsible data usage, as well as shared strategies aimed at improving transparency and reducing bias in NLP models. Additionally, the study explores the broader societal consequences of ethical decision making in NLP, such as impacts on marginalized communities and public trust in automated systems. By integrating diverse scholarly viewpoints, this analysis provides a clearer understanding of the current ethical landscape in NLP research and development. The findings aim to inform future research directions and support the design of more responsible, inclusive, and ethically grounded NLP applications.

Keywords: Ethical considerations, Natural Language Processing(NLP), NLP ethics, Bias, Privacy, Fairness, Transparency, ChatGPT

[This article belongs to Journal of Mobile Computing, Communications & Mobile Networks ]

How to cite this article:
Khushi Singh, Sheetal Singh, Aritro Chakraborty, Himanshu Singh, Sujeet Kumar. Ethical Challenges in Natural Language Processing: A Comparative Study of Solutions Across Multiple Domains. Journal of Mobile Computing, Communications & Mobile Networks. 2026; 13(01):01-07.
How to cite this URL:
Khushi Singh, Sheetal Singh, Aritro Chakraborty, Himanshu Singh, Sujeet Kumar. Ethical Challenges in Natural Language Processing: A Comparative Study of Solutions Across Multiple Domains. Journal of Mobile Computing, Communications & Mobile Networks. 2026; 13(01):01-07. Available from: https://journals.stmjournals.com/jomccmn/article=2026/view=240087


References

  1. Benotti L, Blackburn P. Ethics consideration sections in natural language processing papers. In: Goldberg Y, Kozareva Z, Zhang Y, editors. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing; 2022 Dec; Abu Dhabi, United Arab Emirates. Stroudsburg (PA): Association for Computational Linguistics; 2022. p. 4509–16. doi:10.18653/v1/2022.emnlp-main.299.
  2. Ma Y. A study of ethical issues in natural language processing with artificial intelligence. J Comput Sci Technol Stud. 2023;5(1):52–6. doi:10.32996/jcsts.2023.5.1.7.
  3. Hovy D, Prabhumoye S. Five sources of bias in natural language processing. Lang Linguist Compass. 2021;15(8):e12432. doi:10.1111/lnc3.12432. PubMed PMID: 35864931.
  4. Lalor J, Yang Y, Smith K, Forsgren N, Abbasi A. Benchmarking intersectional biases in NLP. In: Carpuat M, de Marneffe MC, Meza Ruiz IV, editors. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies; 2022 Jul; Seattle, United States. Stroudsburg (PA): Association for Computational Linguistics; 2022. p. 3598–609. doi:10.18653/v1/2022.naacl-main.263.
  5. Shah DS, Schwartz HA, Hovy D. Predictive biases in natural language processing models: A conceptual framework and overview. In: Jurafsky D, Chai J, Schluter N, Tetreault J, editors. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics; 2020 Jul; Online. Stroudsburg (PA): Association for Computational Linguistics; 2020. p. 5248–64. doi:10.18653/v1/2020.acl-main.468.
  6. Akgun S, Greenhow C. Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI Ethics. 2022;2(3):431–40. doi:10.1007/s43681-021-00096-7. PubMed PMID: 34790956.
  7. Huallpa JJ. Exploring the ethical considerations of using ChatGPT in university education. Period Eng Nat Sci. 2023;11(4):105–15. doi:10.21533/pen.v11.i4.200.
  8. Alfano M, Sullivan E, Ebrahimi Fard A. Ethical pitfalls for natural languages processing in psychology. In: Dehghani M, Boyd RL, editors. Handbook of Language Analysis in Psychology. New York: Guilford Press; 2022.
  9. Benotti L, Fort K, Kan MY, Tsvetkov Y. Understanding ethics in NLP authoring and reviewing. In: Zanzotto FM, Pradhan S, editors. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts; 2023 May; Dubrovnik, Croatia. Stroudsburg (PA): Association for Computational Linguistics; 2023. p. 19–24. doi:10.18653/v1/2023.eacl-tutorials.4.
  10. Wang C, Liu S, Yang H, Guo J, Wu Y, Liu J. Ethical considerations of using ChatGPT in health care. J Med Internet Res. 2023;25:e48009. doi:10.2196/48009. PubMed PMID: 37566454.
Regular Issue Subscription Review Article
Volume 13
Issue 01
Received 20/06/2025
Accepted 08/10/2025
Published 15/04/2026
Publication Time 299 Days
135

Login


My IP

PlumX Metrics