Learning to Design Fair and Private Voting Rules
Learning to Design Fair and Private Voting Rules
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DOI：
10.1613/jair.1.13734
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Farhad Mohsin;Ao Liu;Pin-Yu Chen;Francesca Rossi;Lirong Xia
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文献类型：
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作者：
Farhad Mohsin;Ao Liu;Pin-Yu Chen;Francesca Rossi;Lirong Xia
投票被广泛用于根据他们的喜好确定一组代理商的集体决定。在本文中，我们专注于评估和设计投票规则，以支持投票代理人的隐私和对此类代理商的公平概念。为此，我们介绍了一个新颖的团体公平概念，并采用了现有的当地差异隐私概念。然后，我们评估了几个现有投票规则的群体公平水平，以及公平与隐私之间的权衡，表明不可能始终以高公平或高隐私水平获得最大的经济效率。然后，我们既提出机器学习，又提出了一种有限的优化方法来设计新的投票规则，这些规则是公平的，同时保持了高度的经济效率。最后，我们凭经验研究了增加噪音以制定当地差异私人投票规则的效果，并讨论经济效率，公平和隐私之间的三向权衡。本文出现在AI＆Society的特别曲目中。
Voting is used widely to identify a collective decision for a group of agents, based on their preferences. In this paper, we focus on evaluating and designing voting rules that support both the privacy of the voting agents and a notion of fairness over such agents. To do this, we introduce a novel notion of group fairness and adopt the existing notion of local differential privacy. We then evaluate the level of group fairness in several existing voting rules, as well as the trade-offs between fairness and privacy, showing that it is not possible to always obtain maximal economic efficiency with high fairness or high privacy levels. Then, we present both a machine learning and a constrained optimization approach to design new voting rules that are fair while maintaining a high level of economic efficiency. Finally, we empirically examine the effect of adding noise to create local differentially private voting rules and discuss the three-way trade-off between economic efficiency, fairness, and privacy. This paper appears in the special track on AI & Society.