AI Papers Atlas Wave 1
AIORG-W018 Working papers AI × management and organizations

Allocation of Decision Authority to Human and Artificial Intelligence

Susan Carleton Athey · Kevin Bryan · Joshua S. Gans

Also recorded as: AEA Papers and Proceedings 2020 · NBER w26673 · The Allocation of Decision Authority to Human and Artificial Intelligence · doi:10.2139/ssrn.3522322 · doi:10.3386/w26673 · nber:w26673 · ssrn:3522322

economic theory working paper · 2020-01-22

Source of record — nber.org See it on the corpus view
Recorded claims
3
coded from the inspected source
Evidence classes
2
scholarly-full-text, ssrn-abstract-page
Recorded limitations
5
stated, never hidden

From the hash-bound evidence releases

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 apw-r0-seed

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 apw-r0-seed

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

The abstract presents a principal-agent model in which allocating decision authority trades off an AI system's objective alignment against a human agent's incentive to learn decision payoffs; when human effort is desired, delegating authority to the human can be optimal even with a less reliable or biased AI system.

confidence: high ssrn-abstract-page apw-e1-ssrn-backfill

Scope: Formal organizational-economics model; unit: principal, human agent, and AI decision system; no empirical universe, period, or denominator applies.

Structured relationships

In-corpus citations

Only source-supplied cites relationships whose two endpoints are admitted are shown. Invocation evidence remains a separate relationship.

Cites (0)

No in-corpus outgoing citation is available in the current enrichment coverage.

Cited by (0)

No in-corpus incoming citation is available in the current enrichment coverage.

Boundaries

Limitations & independence

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

Recorded limitations

  • Abstract-level evidence only; full text was not inspected.
  • Alignment and effort parameters are not directly estimated
  • Predates modern generative AI and agent products
  • Stylized principal-agent model
  • This is a conditional formal model, not an empirical estimate of organizational outcomes.

Source independence

Independent academic formal model; no empirical source dependence.