Full-Stack Alignment: Co-Aligning AI and Institutions with Thick Models of Value
from Best AI papers explained Hosted by Enoch H. Kang
This research introduces **full-stack alignment (FSA)**, a concept emphasizing the concurrent alignment of **AI systems** and the **institutions** that govern them with **human values**. It argues that current approaches, such as **preferentist modeling of value (PMV)** and **values-as-text (VAT)**, are insufficient because they oversimplify complex human values, leading to undesirable societal outcomes like manipulative AI or misaligned economic incentives. To address these shortcomings, the authors propose **thick models of value (TMV)**, which are structured frameworks for representing values and norms that are robust, can model collective goods, and generalize effectively across contexts. The paper outlines five application areas where TMV can foster beneficial outcomes: **AI value stewardship**, **normatively competent agents**, **win-win AI negotiation**, **meaning-preserving AI economies**, and **democratic regulation at AI speed**.
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