Episodic memory in AI agents poses risks that should be studied and mitigated

2025-01-20

AI Summary

This study addresses emerging safety risks and trustworthiness challenges posed by AI systems with episodic memory capabilities—a domain currently lacking systematic risk characterization. Method: We first establish a comprehensive risk taxonomy for AI episodic memory, identifying novel threats across three dimensions: loss of control, adversarial manipulation, and privacy leakage. Integrating cognitive science modeling, AI safety framework design, and interdisciplinary governance analysis, we propose four principled design guidelines that jointly enhance functionality and ensure robust security. Contribution/Results: We formalize the “memory–credibility” trade-off as a dual-edged dynamic, providing a theoretical foundation and practical roadmap for architecture design, safety evaluation, and regulatory standardization of memory-augmented AI systems—thereby advancing responsible AI development.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Most current AI models have little ability to store and later retrieve a record or representation of what they do. In human cognition, episodic memories play an important role in both recall of the past as well as planning for the future. The ability to form and use episodic memories would similarly enable a broad range of improved capabilities in an AI agent that interacts with and takes actions in the world. Researchers have begun directing more attention to developing memory abilities in AI models. It is therefore likely that models with such capability will be become widespread in the near future. This could in some ways contribute to making such AI agents safer by enabling users to better monitor, understand, and control their actions. However, as a new capability with wide applications, we argue that it will also introduce significant new risks that researchers should begin to study and address. We outline these risks and benefits and propose four principles to guide the development of episodic memory capabilities so that these will enhance, rather than undermine, the effort to keep AI safe and trustworthy.

Problem

• AI memory capability
• safety
• trustworthiness

Innovation

• AI Memory
• Safety Measures
• Reliability Guidelines