Aligned with Whom? Direct and social goals for AI systems

TL;DR

This paper explores the distinction between direct and social alignment problems in AI, emphasizing the importance of governance and norms to ensure AI systems align with individual and societal goals.

Contribution

It introduces a clear framework differentiating direct and social alignment problems, highlighting the need for governance and norm design in addressing social impacts of AI.

Findings

Direct alignment focuses on AI achieving operator goals.

Social alignment involves managing societal impacts and externalities.

Governance and norm enforcement are crucial for social alignment.

Abstract

As artificial intelligence (AI) becomes more powerful and widespread, the AI alignment problem - how to ensure that AI systems pursue the goals that we want them to pursue - has garnered growing attention. This article distinguishes two types of alignment problems depending on whose goals we consider, and analyzes the different solutions necessitated by each. The direct alignment problem considers whether an AI system accomplishes the goals of the entity operating it. In contrast, the social alignment problem considers the effects of an AI system on larger groups or on society more broadly. In particular, it also considers whether the system imposes externalities on others. Whereas solutions to the direct alignment problem center around more robust implementation, social alignment problems typically arise because of conflicts between individual and group-level goals, elevating the importance of AI governance to mediate such conflicts. Addressing the social alignment problem requires both enforcing existing norms on their developers and operators and designing new norms that apply directly to AI systems.