AGENTIC-SYSTEMSJuly 18, 20261 min read

Align AI to Dynamic Human-AI Workflows

Current AI alignment methods fall short by relying on static representations of human preferences. A new paper proposes a paradigm shift toward "interactive and complementary alignment," allowing AI and humans to co-evolve within dynamic workflows.

Computer Science > Artificial Intelligence

Title:Align AI to Dynamic Human-AI Workflows

View PDF HTML (experimental)Abstract:Current alignment approaches typically focus on emulating human behavior using static representations of human preferences, failing to capture the dynamic, context-dependent nature of real-world human-AI interactions. In this paper, we argue for a shift from static and emulative to interactive and complementary alignment, where preferences emerge through interaction and alignment is defined not by satisfying preferences alone. We first formalize this gap by contrasting existing alignment with a trajectory-level view in which human and model behavior co-evolve over time. Because these interaction dynamics have not been adequately captured within existing ML formulations, we ground this perspective in insights from an interdisciplinary workshop. We draw on lessons from social-science accounts of human-human collaboration and then argue that human-AI systems amplify these dynamics, introducing new asymmetries that make reasoning about uncertainty harder and introduce new coordination challenges. Based on these lessons and new challenges, we conclude by outlining a research agenda for developing AI systems that align with humans in interaction, requiring an interdisciplinary synthesis of machine learning and the social and decision sciences.

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Source: arXiv cs.AI Recent

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