As artificial intelligence systems become deeply embedded in our institutions, economies, and personal lives, the challenge of alignment—ensuring AI acts in accordance with human values and societal norms—has become both urgent and complex.

But what exactly should these systems be aligned to—and how do we know we're getting it right? To address this, we turn to a long-standing body of work: how societies have historically measured public preferences and moral norms—and what often goes wrong in the process.

The talk will introduce underutilized datasets—from decades of survey archives to international value studies—that could serve as empirical benchmarks for aligning AI systems with lived human norms. In addition to highlighting valuable data sources, we will examine how lessons from social science can inform the design of human feedback loops in AI. These insights help avoid common pitfalls in capturing human intentions and preferences—such as measurement error, framing effects, and unrepresentative sampling—that have plagued opinion research for decades.

We'll close by addressing the fluid and evolving nature of societal norms, emphasizing the need for alignment strategies that are adaptive to cultural and temporal change. Achieving this kind of adaptability requires not just better data, but durable collaborations between social scientists and machine learning researchers—so that updates to human values can be continuously reflected in system design. The goal is to provoke a deeper, interdisciplinary conversation about what it truly means to align AI with human values—and how to do so responsibly, reliably, and at scale.