Balancing Personalisation and Protection: Ethical and Legal Safeguards for AI-Driven Recommender Systems

  • Pranjal Kumar Azad
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  • Pranjal Kumar Azad

    LL.M. Student at University of Nottingham, U.K

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Abstract

The rapid growth of online content and global internet use has intensified the problem of information overload, prompting widespread adoption of AI driven recommender systems to deliver personalised content. While these systems enhance user experience by reducing search time and improving content relevance, they also raise significant ethical and legal concerns. This paper critically examines the risks posed by general purpose AI recommender systems, particularly those deployed on social media platforms, including privacy violations, algorithmic bias and behavioural manipulation. It further explores associated legal challenges such as data protection compliance, transparency obligations and intellectual property rights. Drawing on both technological analysis and policy perspectives, the study proposes practical measures for mitigating these risks, including regulatory reforms, enhanced transparency and systematic auditing. By addressing the dual imperative of fostering innovation and safeguarding users, this work offers a framework for policymakers, industry stakeholders and researchers to ensure that AI powered recommendations serve the public interest without undermining fundamental rights.

Keywords

  • Recommender systems
  • AI-driven
  • data protection
  • transparency

Type

Research Paper

Information

International Journal of Law Management and Humanities, Volume 8, Issue 4, Page 2412 - 2427

DOI: https://doij.org/10.10000/IJLMH.1110662

Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution -NonCommercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/), which permits remixing, adapting, and building upon the work for non-commercial use, provided the original work is properly cited.

Copyright

Copyright © IJLMH 2021