An experiment on algorithmic mediation

Mediation is widely used because it is faster, cheaper, and more flexible than courts. Yet a growing body of evidence suggests that the informality of mediation can disproportionately disadvantage less powerful parties, particularly those marginalized by gender, race, or ethnicity. In response, demand for more structured and rigorous mediation practices, combined with recent technological advances, has spurred the growth of Online Dispute Resolution (ODR) systems. In a standard ODR system, the mediator is an algorithm defined by predetermined rules. Although these protocols are designed to deliver impartial outcomes, we know little about how gender visibility shapes behavior and outcomes in algorithmic mediation. We study this question in a controlled experiment using the algorithmic mediation mechanism proposed by \citet{kesten2025strategy}. We measure negotiation behavior, including participants’ demands, acceptance decisions, and beliefs about others’ behavior, and we collect detailed perceptions of fairness and the salience of gender in decision-making. We manipulate gender visibility across two experimental conditions to examine its effects on evaluation outcomes. We further compare this algorithmic protocol against free-form (unstructured) bargaining, in which participants exchange offers directly without an algorithm. Crossing the two protocols with gender visibility allows us to test whether algorithmic mediation narrows or widens gender-based differences in negotiation outcomes relative to unstructured bargaining.