Evolution of fairness in hybrid populations with specialised AI agents
Zhao Song, Theodor Cimpeanu, Chen Shen, The Anh Han

TL;DR
This paper investigates how asymmetric AI roles in hybrid societies influence fairness, showing that strategic AI gatekeepers outperform generous AI hosts in promoting equitable outcomes in social interactions like the Ultimatum Game.
Contribution
It introduces a bipartite hybrid population model with specialized AI agents, revealing the effectiveness of strategic AI gatekeepers over unconditional fairness models.
Findings
Samaritan AI receivers effectively promote fairness.
Discriminatory AI proposers outperform Samaritan proposers.
Strategic AI enforcement reduces the critical mass needed for fairness.
Abstract
Fairness in hybrid societies hinges on a simple choice: should AI be a generous host or a strict gatekeeper? Moving beyond symmetric models, we show that asymmetric social structures--like those in hiring, regulation, and negotiation--AI that guards fairness outperforms AI that gifts it. We bridge this gap with a bipartite hybrid population model of the Ultimatum Game, separating humans and AI into distinct proposer and receiver groups. We first introduce Samaritan AI agents, which act as either unconditional fair proposers or strict receivers. Our results reveal a striking asymmetry: Samaritan AI receivers drive population-wide fairness far more effectively than Samaritan AI proposers. To overcome the limitations of the Samaritan AI proposer, we design the Discriminatory AI proposer, which predicts co-players' expectations and only offers fair portions to those with high acceptance…
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Taxonomy
TopicsEthics and Social Impacts of AI · Evolutionary Game Theory and Cooperation · Experimental Behavioral Economics Studies
