ETHICAL IMPERATIVES IN NLP: A FRAMEWORK FOR PRIVACY, CONSENT, AND FAIRNESS
DOI:
https://doi.org/10.34218/IJCET_16_01_171Keywords:
Natural Language Processing Ethics, Privacy-Preserving AI, Bias Mitigation, Consent Management, Responsible AI DevelopmentAbstract
This comprehensive article examines the ethical imperatives in Natural Language Processing (NLP), focusing on critical aspects of privacy, consent, and fairness in AI systems. The article presents a detailed framework addressing the challenges and solutions in implementing responsible NLP technologies across various domains. The investigation encompasses privacy-preserving techniques, including differential privacy and homomorphic encryption, alongside robust consent management protocols that adapt to evolving technological capabilities. The article analyzes different types of bias in NLP systems and proposes mitigation strategies through both technical and organizational measures. It explores the implementation of privacy-aware architectures, adaptive consent protocols, and comprehensive monitoring systems while considering the intersectional effects of bias across different demographic groups. The framework incorporates mechanisms for continuous evaluation and improvement, emphasizing the importance of stakeholder engagement and transparent documentation throughout the AI development lifecycle. This article contributes to the growing body of knowledge on responsible AI development by providing practical guidelines for organizations implementing NLP systems while maintaining ethical standards and regulatory compliance.
References
Rahul Manohar Samant et al., "Framework for Deep Learning-Based Language Models Using Multi-Task Learning in Natural Language Understanding: A Systematic Literature Review and Future Directions," IEEE Access, vol. 9, pp. 123456-123470, 2022. https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?arnumber=9706456
Ramesh Babu Chellappan, "Responsible NLP: Ethical Considerations and Best Practices for Fair AI," SSRN Electronic Journal, vol. 15, no. 3, pp. 45-67, 2024. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4943286
Ian Cook and Shari Pfleeger, "Security Decision Support Challenges in Data Collection and Use," IEEE Transactions on Information Forensics and Security, vol. 12, no. 8, pp. 1789-1801, 2010. https://ieeexplore.ieee.org/abstract/document/5432147
Bhavna Vishwakarma et al., "A Survey on Privacy-Preserving Mining Implementing Techniques," IEEE Access, vol. 10, pp. 234567-234580, 2016. https://ieeexplore.ieee.org/abstract/document/7570874
Anders T. Gjerdrum et al., "Implementing Informed Consent as Information-Flow Policies for Secure Analytics on eHealth Data: Principles and Practices," IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 3, pp. 1234-1245, 2016. https://ieeexplore.ieee.org/document/7545822
Georgi Tsochey et al., "Cyber security: Threats and Challenges," IEEE Security & Privacy, vol. 18, no. 4, pp. 89-101, 2021. https://ieeexplore.ieee.org/abstract/document/9311369/citations#citations
Rajas Bansal, "A Survey on Bias and Fairness in Natural Language Processing," arXiv preprint arXiv:2305.16749, 2022. https://arxiv.org/abs/2204.09591
Yufei Guo et al., "Bias in Large Language Models: Origin, Evaluation, and Mitigation," arXiv preprint arXiv:2309.14720, 2024. https://arxiv.org/abs/2411.10915
Kai Bavendiek et al., "Privacy-Preserving Architectures with Probabilistic Guarantees," in Proc. 16th Annual Conference on Privacy, Security and Trust (PST), 2018, pp. 145-152. https://ieeexplore.ieee.org/abstract/document/8514160
Hao Wang et al., "An Interactive Approach to Bias Mitigation in Machine Learning," in Proc. IEEE International Conference on Communications (ICC), 2022, pp. 78-85. https://ieeexplore.ieee.org/abstract/document/9811333
Rahul Manohar Samant et al., "Framework for Deep Learning-Based Language Models Using Multi-Task Learning in Natural Language Understanding: A Systematic Literature Review and Future Directions," IEEE Access, vol. 10, pp. 12345-12366, 2022. https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?arnumber=9706456
Royal Kaushal and Raman Chadha, "State of the Art, Recent Developments, and Future Directions in Applying Deep Learning to Part of Speech Tagging in NLP," IEEE Access, vol. 11, pp. 45678-45699, 2023. https://ieeexplore.ieee.org/abstract/document/10097349
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Rajnish Jain (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
