Rethinking How AI Embeds and Adapts to Human Values: Challenges and Opportunities

TL;DR

This paper emphasizes the importance of rethinking value alignment in AI, advocating for dynamic, adaptable systems that consider diverse human values and multi-agent interactions to reduce risks and improve alignment.

Contribution

It proposes moving beyond static value models, incorporating long-term reasoning, and utilizing multi-agent frameworks to better address the complexity of human values in AI systems.

Findings

Highlights challenges in current value alignment approaches.

Suggests multi-agent systems as a framework for pluralism.

Identifies future directions for research in value alignment.

Abstract

The concepts of ``human-centered AI'' and ``value-based decision'' have gained significant attention in both research and industry. However, many critical aspects remain underexplored and require further investigation. In particular, there is a need to understand how systems incorporate human values, how humans can identify these values within systems, and how to minimize the risks of harm or unintended consequences. In this paper, we highlight the need to rethink how we frame value alignment and assert that value alignment should move beyond static and singular conceptions of values. We argue that AI systems should implement long-term reasoning and remain adaptable to evolving values. Furthermore, value alignment requires more theories to address the full spectrum of human values. Since values often vary among individuals or groups, multi-agent systems provide the right framework for navigating pluralism, conflict, and inter-agent reasoning about values. We identify the challenges associated with value alignment and indicate directions for advancing value alignment research. In addition, we broadly discuss diverse perspectives of value alignment, from design methodologies to practical applications.