Towards Integrated Alignment

TL;DR

This paper advocates for an integrated approach to AI alignment, combining diverse methods and fostering collaboration to address the fragmentation and vulnerabilities in current alignment strategies.

Contribution

It introduces a set of design principles for developing Integrated Alignment frameworks inspired by immunology and cybersecurity, emphasizing diversity and collaboration.

Findings

Proposes combining alignment approaches for robustness.

Highlights importance of strategic diversity in alignment.

Recommends open collaboration and resource sharing.

Abstract

As AI adoption expands across human society, the problem of aligning AI models to match human preferences remains a grand challenge. Currently, the AI alignment field is deeply divided between behavioral and representational approaches, resulting in narrowly aligned models that are more vulnerable to increasingly deceptive misalignment threats. In the face of this fragmentation, we propose an integrated vision for the future of the field. Drawing on related lessons from immunology and cybersecurity, we lay out a set of design principles for the development of Integrated Alignment frameworks that combine the complementary strengths of diverse alignment approaches through deep integration and adaptive coevolution. We highlight the importance of strategic diversity - deploying orthogonal alignment and misalignment detection approaches to avoid homogeneous pipelines that may be "doomed to success". We also recommend steps for greater unification of the AI alignment research field itself, through cross-collaboration, open model weights and shared community resources.