Navigating the Complex Landscape of AI-Driven Personalization: Challenges and Considerations in the Generative AI Era

Ankur Aggarwal

doi:10.32628/CSEIT2410612424

Authors

Ankur Aggarwal Google, USA Author

DOI:

https://doi.org/10.32628/CSEIT2410612424

Keywords:

Generative AI Personalization, Privacy-Preserving Machine Learning, Algorithmic Bias Mitigation, Contextual Dynamics, User Autonomy

Abstract

The emergence of generative AI has fundamentally transformed personalization systems, creating both unprecedented opportunities and significant challenges for organizations. This article examines the complex landscape of AI-driven personalization, focusing on four critical areas: privacy preservation, algorithmic bias mitigation, contextual dynamics, and user autonomy. Through analysis of industry practices, we explore how organizations are navigating these challenges while implementing effective personalization solutions. The article presents findings on privacy-first architectures, bias mitigation frameworks, adaptive system designs, and user empowerment tools, highlighting both technical and ethical considerations. The comprehensive review demonstrates that successful implementation of AI personalization systems requires a balanced approach that addresses privacy concerns while maintaining system effectiveness, mitigates algorithmic bias while preserving performance, adapts to evolving user contexts, and preserves user autonomy while delivering personalized experiences

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