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Explainable Voting
Dominik Peters · Ariel Procaccia · Alexandros Psomas · Zixin Zhou

Thu Dec 10 09:00 AM -- 11:00 AM (PST) @ Poster Session 5 #1560
The design of voting rules is traditionally guided by desirable axioms. Recent work shows that, surprisingly, the axiomatic approach can also support the generation of explanations for voting outcomes. However, no bounds on the size of these explanations is given; for all we know, they may be unbearably tedious. We prove, however, that outcomes of the important Borda rule can be explained using $O(m^2)$ steps, where $m$ is the number of alternatives. Our main technical result is a general lower bound that, in particular, implies that the foregoing bound is asymptotically tight. We discuss the significance of our results for AI and machine learning, including their potential to bolster an emerging paradigm of automated decision making called virtual democracy.

Author Information

Dominik Peters (Carnegie Mellon University)
Ariel Procaccia (Harvard University)
Alexandros Psomas (Purdue University)
Zixin Zhou (Peking University)

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