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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. 

We introduce the Online Preference Reporting and Aggregation (OPRA) system, an open-source online system that aims at providing support for group decision-making. We illustrate OPRA's distinctive features: UI for reporting rankings with ties, comprehensive analytics of preferences, and group decision-making in combinatorial domains. We also discuss our work in an automatic mentor matching system. We hope that the open-source nature of OPRA will foster development of computerized group decision support systems. 

In fair division,equitabilitydictates that each partic-ipant receives the same level of utility. In this work,we study equitable allocations of indivisible goodsamong agents with additive valuations. While priorwork has studied (approximate) equitability in iso-lation, we consider equitability in conjunction withother well-studied notions of fairness and economicefficiency. We show that the Leximin algorithm pro-duces an allocation that satisfies equitability up toany good and Pareto optimality. We also give anovel algorithm that guarantees Pareto optimalityand equitability up to one good in pseudopolyno-mial time. Our experiments on real-world prefer-ence data reveal that approximate envy-freeness, ap-proximate equitability, and Pareto optimality canoften be achieved simultaneously.