AIORG-W018 Working papers

Allocation of Decision Authority to Human and Artificial Intelligence

NBER w26673 · AEA Papers and Proceedings 2020

economic theory working paper

Recorded claims
2
coded from the inspected source
Evidence classes
1
scholarly-full-text
Recorded limitations
3
stated, never hidden

From the wave-0 evidence ledger

Recorded claims

Each claim states what the inspected source says, at the recorded location—bounded by its scope and graded by its confidence. Nothing here is a synthesis across works.

The model frames decision authority as a trade-off between an AI system's alignment with the principal's objective and a human agent's ability and incentive to acquire decision-relevant information.

confidence: high as statement of model; not empirical scholarly-full-text

Scope: Stylized authority-allocation mechanism, not measured organizational behavior.

Even when AI has strong decision quality, assigning authority to a human can be optimal when human authority preserves information-acquisition effort; therefore prediction accuracy alone does not determine an efficient accountability boundary.

confidence: high as conditional model result scholarly-full-text

Scope: Under the model's assumptions; requires empirical calibration before application.

Boundaries

Limitations & independence

Recorded at coding time, carried with the work forever. A claim without its limits is not evidence.

Recorded limitations

  • Stylized principal-agent model
  • Alignment and effort parameters are not directly estimated
  • Predates modern generative AI and agent products

Source independence

Independent academic formal model; no empirical source dependence.