  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.
