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1. Fairer Together: Mitigating Disparate Exposure in Kemeny Rank Aggregation

   https://doi.org/10.1145/3593013.3594085  

   Cachel, Kathleen; Rundensteiner, Elke (June 2023, 2023 ACM Conference on Fairness, Accountability, and Transparency)

   In social choice, traditional Kemeny rank aggregation combines the preferences of voters, expressed as rankings, into a single consensus ranking without consideration for how this ranking may unfairly affect marginalized groups (i.e., racial or gender). Developing fair rank aggregation methods is critical due to their societal influence in applications prioritizing job applicants, funding proposals, and scheduling medical patients. In this work, we introduce the Fair Exposure Kemeny Aggregation Problem (FairExp-kap) for combining vast and diverse voter preferences into a single ranking that is not only a suitable consensus, but ensures opportunities are not withheld from marginalized groups. In formalizing FairExp-kap, we extend the fairness of exposure notion from information retrieval to the rank aggregation context and present a complimentary metric for voter preference representation. We design algorithms for solving FairExp-kap that explicitly account for position bias, a common ranking-based concern that end-users pay more attention to higher ranked candidates. epik solves FairExp-kap exactly by incorporating non-pairwise fairness of exposure into the pairwise Kemeny optimization; while the approximate epira is a candidate swapping algorithm, that guarantees ranked candidate fairness. Utilizing comprehensive synthetic simulations and six real-world datasets, we show the efficacy of our approach illustrating that we succeed in mitigating disparate group exposure unfairness in consensus rankings, while maximally representing voter preferences. 

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2. Near Instance Optimal Model Selection for Pure Exploration Linear Bandits

   Zhu, Yinglun; Katz-Samuels, Julian; Nowak, Robert (January 2022, International Conference on Artificial Intelligence and Statistics)

   The model selection problem in the pure exploration linear bandit setting is introduced and studied in both the fixed confidence and fixed budget settings. The model selection problem considers a nested sequence of hypothesis classes of increasing complexities. Our goal is to automatically adapt to the instance-dependent complexity measure of the smallest hypothesis class containing the true model, rather than suffering from the complexity measure related to the largest hypothesis class. We provide evidence showing that a standard doubling trick over dimension fails to achieve the optimal instance-dependent sample complexity. Our algorithms define a new optimization problem based on experimental design that leverages the geometry of the action set to efficiently identify a near-optimal hypothesis class. Our fixed budget algorithm uses a novel application of a selection-validation trick in bandits. This provides a new method for the understudied fixed budget setting in linear bandits (even without the added challenge of model selection). We further generalize the model selection problem to the misspecified regime, adapting our algorithms in both fixed confidence and fixed budget settings. 

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3. Singularly Optimal Randomized Leader Election

   https://doi.org/10.4230/LIPIcs.DISC.2020.22  

   Kutten, Shay; Moses Jr., William K.; Pandurangan, Gopal; Peleg, David (January 2020, 34th International Symposium on Distributed Computing (DISC 2020))

   null (Ed.)

   This paper concerns designing distributed algorithms that are singularly optimal, i.e., algorithms that are simultaneously time and message optimal, for the fundamental leader election problem in networks. Our main result is a randomized distributed leader election algorithm for asynchronous complete networks that is essentially (up to a polylogarithmic factor) singularly optimal. Our algorithm uses O(n) messages with high probability and runs in O(log² n) time (with high probability) to elect a unique leader. The O(n) message complexity should be contrasted with the Ω(n log n) lower bounds for the deterministic message complexity of leader election algorithms (regardless of time), proven by Korach, Moran, and Zaks (TCS, 1989) for asynchronous algorithms and by Afek and Gafni (SIAM J. Comput., 1991) for synchronous networks. Hence, our result also separates the message complexities of randomized and deterministic leader election. More importantly, our (randomized) time complexity of O(log² n) for obtaining the optimal O(n) message complexity is significantly smaller than the long-standing Θ̃(n) time complexity obtained by Afek and Gafni and by Singh (SIAM J. Comput., 1997) for message optimal (deterministic) election in asynchronous networks. Afek and Gafni also conjectured that Θ̃(n) time would be optimal for message-optimal asynchronous algorithms. Our result shows that randomized algorithms are significantly faster. Turning to synchronous complete networks, Afek and Gafni showed an essentially singularly optimal deterministic algorithm with O(log n) time and O(n log n) messages. Ramanathan et al. (Distrib. Comput. 2007) used randomization to improve the message complexity, and showed a randomized algorithm with O(n) messages but still with O(log n) time (with failure probability O(1 / log^{Ω(1)}n)). Our second result shows that synchronous complete networks admit a tightly singularly optimal randomized algorithm, with O(1) time and O(n) messages (both bounds are optimal). Moreover, our algorithm’s time bound holds with certainty, and its message bound holds with high probability, i.e., 1-1/n^c for constant c. Our results demonstrate that leader election can be solved in a simultaneously message and time-efficient manner in asynchronous complete networks using randomization. It is open whether this is possible in asynchronous general networks. 

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4. Election Control by Manipulating Issue Significance

   Estornell, Andrew; Das, Sanmay; Elkind, Edith; Vorobeychik, Yevgeniy (January 2020, Conference on Uncertainty in Artificial Intelligence (UAI))

   Integrity of elections is vital to democratic systems, but it is frequently threatened by malicious actors. The study of algorithmic complexity of the problem of manipulating election outcomes by changing its structural features is known as election control. One means of election control that has been proposed is to select a subset of issues that determine voter preferences over candidates. We study a variation of this model in which voters have judgments about relative importance of issues, and a malicious actor can manipulate these judgments. We show that computing effective manipulations in this model is NP-hard even with two candidates or binary issues. However, we demonstrate that the problem is tractable with a constant number of voters or issues. Additionally, while it remains intractable when voters can vote stochastically, we exhibit an important special case in which stochastic voting enables tractable manipulation. 

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5. Election Control by Manipulating Issue Significance

   Estornell, Andrew; Das, Sanmay; Elkind, Edith; Vorobeychik, Yevgeniy (January 2020, Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence)

   Integrity of elections is vital to democratic systems, but it is frequently threatened by malicious actors.The study of algorithmic complexity of the problem of manipulating election outcomes by changing its structural features is known as election control Rothe [2016].One means of election control that has been proposed, pertinent to the spatial voting model, is to select a subset of issues that determine voter preferences over candidates.We study a variation of this model in which voters have judgments about relative importance of issues, and a malicious actor can manipulate these judgments.We show that computing effective manipulations in this model is NP-hard even with two candidates or binary issues.However, we demonstrate that the problem becomes tractable with a constant number of voters or issues.Additionally, while it remains intractable when voters can vote stochastically, we exhibit an important special case in which stochastic voting behavior enables tractable manipulation. 

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