# Delegation and Verification Under AI

arXiv: https://arxiv.org/abs/2603.02961 · HTML: https://mlsanity.com/paper/2603.02961/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 3 Mar 2026.

## Builds on (most cited first, 6 of 6)

- [Consistent Estimators for Learning to Defer to an Expert](https://mlsanity.com/paper/2006.01862/) (2020)
- [Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer](https://mlsanity.com/paper/1711.06664/) (2017)
- [Human-AI Collaboration: The Effect of AI Delegation on Human Task Performance and Task Satisfaction](https://mlsanity.com/paper/2303.09224/) (2023)
- [A Decision Theoretic Framework for Measuring AI Reliance](https://mlsanity.com/paper/2401.15356/) (2024)
- [Intelligent AI Delegation](https://mlsanity.com/paper/2602.11865/) (2026)
- [A Mathematical Framework for AI-Human Integration in Work](https://mlsanity.com/paper/2505.23432/) (2025)

## Built on by (most cited first, 1 of 1)

- [Post-AGI Economies: Autonomy and the First Fundamental Theorem of Welfare Economics](https://mlsanity.com/paper/2604.21216/) (2026)

## Abstract (arXiv)

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.