Adversarial Attacks on Fairness of Graph Neural Networks
Binchi Zhang, Yushun Dong, et al.
ICLR 2024
The (group) no-show paradox refers to the undesirable situation where a group of agents have incentive to abstain from voting to make the winner more favorable to them. To understand whether it is a critical concern in practice, in this paper, we take a computational approach by examining the computational complexity of verifying whether the group no-show paradox exists given agents’ preferences and the voting rule. We prove that, unfortunately, the verification problem is NP-hard to compute for some commonly studied voting rules, i.e., Copeland, maximin, single transferable vote, and all Condorcetified positional scoring rules such as Black’s rule. We propose integer linear programming-based algorithms and a search-based algorithm for the verification problem for different voting rules. Experimental results on synthetic data illustrate that the former is efficient when the number of unique rankings in a profile is not too high, and the latter is efficient for a small number of agents. With the help of these algorithms, we observe that group no-show paradoxes rarely occur in real-world data.
Binchi Zhang, Yushun Dong, et al.
ICLR 2024
Natalia Martinez Gil, Kanthi Sarpatwar, et al.
NeurIPS 2023
Philips George John, Deepak Vijaykeerthy, et al.
UAI 2020
Dennis Wei, Karthikeyan Natesan Ramamurthy, et al.
AISTATS 2020