Working Paper: CEPR ID: DP17298
Authors: Anton Korinek; Avital Balwit
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.
Keywords: agency theory; delegation; direct alignment; social alignment; AI governance
JEL Codes: D6; O3
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Operator's goals (L21) | AI's actions (Y20) |
| Failures in direct alignment (Y80) | Unintended consequences (D62) |
| AI's actions (Y20) | Externalities (D62) |
| Societal norms (Z13) | AI's actions (Y20) |
| Regulatory solutions (G18) | Social alignment (Z13) |
| Social alignment (Z13) | Welfare of all affected parties (I38) |
| AI systems (C45) | Political polarization (D72) |