A Statistical Case Against Empirical Human-AI Alignment
Julian Rodemann, Esteban Garces Arias, Luther, Christoph, Christoph Jansen, Thomas Augustin · arXiv (Cornell University) · 2025
Empirical human-AI alignment aims to make AI systems act in line with observed human behavior. While noble in its goals, we argue that empirical alignment can inadvertently introduce statistical biases that warrant caution. This position paper thus advocates against naive empirical alignment, offering prescriptive alignment and a posteriori empirical alignment as alternatives. We substantiate our principled argument by tangible examples like human-centric decoding of language models.