Justifying Social-Choice Mechanism Outcome for Improving Participant Satisfaction

TL;DR

This study investigates how automatically generated explanations based on mechanism features can improve participant satisfaction and acceptance in social-choice mechanisms, matching the effectiveness of crowdsourced explanations while reducing costs.

Contribution

The paper introduces an automatic explanation generation method for social-choice outcomes, demonstrating its effectiveness in enhancing satisfaction and acceptance comparable to crowdsourcing.

Findings

Explanations increase satisfaction and acceptance, especially for least preferred outcomes.

Automatically generated explanations perform as well as crowdsourced ones in user satisfaction.

Automated explanations reduce belief that a different winner was more appropriate.

Abstract

In many social-choice mechanisms the resulting choice is not the most preferred one for some of the participants, thus the need for methods to justify the choice made in a way that improves the acceptance and satisfaction of said participants. One natural method for providing such explanations is to ask people to provide them, e.g., through crowdsourcing, and choosing the most convincing arguments among those received. In this paper we propose the use of an alternative approach, one that automatically generates explanations based on desirable mechanism features found in theoretical mechanism design literature. We test the effectiveness of both of the methods through a series of extensive experiments conducted with over 600 participants in ranked voting, a classic social choice mechanism. The analysis of the results reveals that explanations indeed affect both average satisfaction from and acceptance of the outcome in such settings. In particular, explanations are shown to have a positive effect on satisfaction and acceptance when the outcome (the winning candidate in our case) is the least desirable choice for the participant. A comparative analysis reveals that the automatically generated explanations result in similar levels of satisfaction from and acceptance of an outcome as with the more costly alternative of crowdsourced explanations, hence eliminating the need to keep humans in the loop. Furthermore, the automatically generated explanations significantly reduce participants' belief that a different winner should have been elected compared to crowdsourced explanations.