Two Perspectives on AI's Frontier: Agentic Accountability and Temporal Limits in LLMs
The Communications of the ACM has published two thought-provoking pieces that illuminate different dimensions of current AI limitations and challenges.
Agentic AI and Accountability
The first article, "Agentic AI: The Buck Stops Where?," tackles the thorny question of accountability as AI systems grow more autonomous. As AI agents gain the ability to plan, execute, and adapt without constant human intervention, traditional frameworks for assigning responsibility become strained. The piece explores where accountability should rest—whether with developers, deployers, or the AI systems themselves—and the legal and ethical implications of increasingly autonomous decision-making systems.
LLMs and Temporal Understanding
The second article, "Are LLMs Stuck in Time?," examines a fundamental limitation in large language models: their relationship with time. LLMs are typically trained on historical data up to a certain cutoff point, raising questions about their ability to reason about temporal sequences, understand current events, or maintain awareness of the present moment. This exploration touches on both technical aspects of how these models process and represent time, as well as practical implications for applications requiring up-to-date knowledge.
Together, these pieces highlight two interconnected challenges facing AI practitioners: building appropriate oversight mechanisms for autonomous systems while understanding the inherent constraints of the models being deployed. Both articles appear in the academic literature, suggesting these are active areas of research with implications for both theory and practice in AI development.