Delegation and Verification Under AI

TL;DR

This paper models how AI influences worker delegation and verification decisions within institutions, revealing phase transitions and disparities in worker quality based on verification ability, with implications for institutional outcomes.

Contribution

It introduces a formal model of delegation and verification under AI, highlighting phase transitions and the impact of verification ability on worker quality and disparities.

Findings

AI induces phase transitions in worker behavior.

Strong verification reliability amplifies worker quality.

AI can degrade institutional worker quality for less reliable workers.

Abstract

As AI systems enter institutional workflows, workers must decide whether to delegate task execution to AI and how much effort to invest in verifying AI outputs, while institutions evaluate workers using outcome-based standards that may misalign with workers' private costs. We model delegation and verification as the solution to a rational worker's optimization problem, and define worker quality by evaluating an institution-centered utility (distinct from the worker's objective) at the resulting optimal action. We formally characterize optimal worker workflows and show that AI induces *phase transitions*, where arbitrarily small differences in verification ability lead to sharply different behaviors. As a result, AI can amplify workers with strong verification reliability while degrading institutional worker quality for others who rationally over-delegate and reduce oversight, even when baseline task success improves and no behavioral biases are present. These results identify a structural mechanism by which AI reshapes institutional worker quality and amplifies quality disparities between workers with different verification reliability.