Human Oversight and Aversion to AI Redistributive Decisions

Participants evaluate redistributive allocations that determine other people’s payoffs. Each allocation is labeled as being made by either a human or an AI system. Participants can either accept the allocation or pay a small fee to obtain additional information and revise it. The sole intervention is the different nature of the decision-maker (human vs AI) whose choice is evaluated.

2025-09-21

2025-10-21

The main outcome is the intervention rate.
Intervene(i,a): Outcome variable on the extensive margin. It is a dummy variable equal to 1 if the reviewer decides to pay the fee and intervene, revealing the workers’ initial criterion (Luck or Merit), and 0 otherwise. I collect one observation for each observed allocation a ((6, 0), (5, 1), (4, 2), (3, 3)) through the strategy method, and the reviewer is exposed to both treatment conditions (human and AI), thus there will be eight observed allocations.
∆G(i,a): Outcome variable on the intensive margin. It measures the change in the Gini index before and after the reviewer’s intervention. It is equal to 0 if the reviewer decides not to intervene.

Probability of Merit scenario (pM): elicited probability that the workers are in the Merit scenario, conditional on observed allocation (a) and spectator’s type (tau): pM = P(M | a, tau ). Consequently, pL = 1 − pM is the elicited probability that workers are in the Luck scenario. I elicit eight probabilities per subject: (AI or Human) X {(6,0), (5,1), (4,2), (3,3)}

The experiment has four parts. First, workers earn money in a real-effort task. Second, spectators decide how to redistribute earnings within a randomly drawn worker pair. Third, reviewers observe a spectator’s decision and decide whether to intervene and modify the allocation. Fourth, workers are paid based on the final redistribution. The study focuses on reviewers’ decisions; workers and spectators create a consequential economic environment.

Workers
Workers are recruited on Prolific. After the task, they are randomly paired; in each pair, one worker receives an additional reward determined either by Merit (higher performance) or by Luck (random draw), with equal probability across pairs. They are informed that a third party (the spectator) will see the initial earnings and the criterion and may redistribute earnings within the pair.

Spectators
There are two spectator types: human (participants recruited on Prolific) and AI (ChatGPT-5). Both receive identical instructions and information. Each worker pair is evaluated by both spectator types. Spectators choose whether and how to redistribute the initial earnings. They make the decision for a pair in the Merit condition, and for a pair in the Luck condition.

Reviewers
The same human participants return one week later as reviewers. They evaluate a spectator’s redistribution under incomplete information: they see only the final allocation and whether it was made by a human or an AI spectator (not whether initial earnings came from Merit or Luck). Reviewers can either accept the allocation or intervene by paying a small fee to reveal the criterion and then revise the allocation. Choices are elicited for a fixed set of canonical allocations in both spectator types, with randomized order. One decision is randomly selected for payment; participants remain anonymous and are never matched to their own earlier spectator decisions.

Belief Elicitation
Beliefs about the source of inequality (Merit vs Luck), conditional on the observed allocation and spectator type, are elicited with an incentivized measure.

Not available

Randomization is done through the experimental software (oTree). Half of the reviewers (150) will first see the redistributions made by AI, then those made by humans; the other half, the opposite order.

Randomization happens at the individual level. First, human spectators are randomized in the belief elicitation: half (150) will see AI first, half (150) human first. Then, when they participate as reviewers one week later, these two subgroups are equally randomized in the treatment order: half (75 + 75) will first see the redistributions made by AI, then those made by humans; the other half, the opposite order.

Yes

300 individuals.

2400 observations = 300 individuals X 4 allocations X 2 treatment conditions.

150 participants (600 observations) with AI first, 150 participants (600 observations) with human first (between-subjects).

I consider the more conservative between-subjects comparison. If we assume a sample size of 300 subjects (150 independent observations per treatment), a non-parametric test as the Wilcoxon-Mann-Whitney test achieves a minimum detectable effect of 0.35 s.d. with a two-tailed test, alpha=0.05, power=0.80, and without any assumption on the parent distribution (min A.R.E.).