Balancing Minds and Data: The Privacy Dilemma of LLMs and Anthropomorphism in LLMs
R. L. Meier · Journal of Social Computing · 2025
This essay examines the intricate relationship between large language models (LLMs) and privacy, investigating the ethical and practical issues stemming from cutting-edge artificial intelligence (AI) technologies. The research delves into the evolving understanding of privacy in the digital era, with a specific emphasis on the risks posed by anthropomorphic AI design. The analysis highlights critical privacy concerns: (1) Trust and accountability: The lack of true moral agency in AI systems complicates traditional notions of trust and responsibility; (2) Nissenbaum's Contextual Integrity Framework as a tool to explore privacy issues in general and with LLM; (3) Data collection challenges: LLMs collect extensive user data, often without explicit consent, potentially breaching contextual privacy norms; (4) Anthropomorphism risks: Human-like AI interfaces can foster over-trust, leading users to share sensitive information inappropriately. This article underscores that privacy is a complex, multidimensional concept profoundly shaped by technological, cultural, and social forces. As AI technologies continue to advance, safeguarding privacy will necessitate a nuanced approach that strikes a balance between individual rights, societal needs, and technological progress. We conclude with user-oriented guidelines and future research directions, offering a comprehensive framework for understanding and addressing the privacy implications of LLMs.