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In many social-choice mechanisms the resulting choice is not the mostpreferred one for some of the participants, thus the need for methods tojustify the choice made in a way that improves the acceptance and satisfactionof said participants. One natural method for providing such explanations is toask people to provide them, e.g., through crowdsourcing, and choosing the mostconvincing arguments among those received. In this paper we propose the use ofan alternative approach, one that automatically generates explanations based ondesirable mechanism features found in theoretical mechanism design literature.We test the effectiveness of both of the methods through a series of extensiveexperiments conducted with over 600 participants in ranked voting, a classicsocial choice mechanism. The analysis of the results reveals that explanationsindeed affect both average satisfaction from and acceptance of the outcome insuch settings. In particular, explanations are shown to have a positive effecton satisfaction and acceptance when the outcome (the winning candidate in ourcase) is the least desirable choice for the participant. A comparative analysisreveals that the automatically generated explanations result in similar levelsof satisfaction from and acceptance of an outcome as with the more costlyalternative of crowdsourced explanations, hence eliminating the need to keephumans in the loop. Furthermore, the automatically generated explanationssignificantly reduce participants' belief that a different winner should havebeen elected compared to crowdsourced explanations.

Justifying Social-Choice Mechanism Outcome for Improving Participant Satisfaction