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1. Incentivizing Recourse through Auditing in Strategic Classification

   Estornell, Andrew ; Chen, Yatong ; Das, Sanmay ; Liu, Yang ; Vorobeychik, Yevgeniy ( August 2023 , International Joint Conference on Artificial Intelligence)

   The increasing automation of high-stakes decisions with direct impact on the lives and well-being of individuals raises a number of important considerations. Prominent among these is strategic behavior by individuals hoping to achieve a more desirable outcome. Two forms of such behavior are commonly studied: 1) misreporting of individual attributes, and 2) recourse, or actions that truly change such attributes. The former involves deception, and is inherently undesirable, whereas the latter may well be a desirable goal insofar as it changes true individual qualification. We study misreporting and recourse as strategic choices by individuals within a unified framework. In particular, we propose auditing as a means to incentivize recourse actions over attribute manipulation, and characterize optimal audit policies for two types of principals, utility-maximizing and recourse-maximizing. Additionally, we consider subsidies as an incentive for recourse over manipulation, and show that even a utility-maximizing principal would be willing to devote a considerable amount of audit budget to providing such subsidies. Finally, we consider the problem of optimizing fines for failed audits, and bound the total cost incurred by the population as a result of audits. 

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2. Understanding the Effect of Task Complexity and Problem-Solving Skills on the Design Performance of Agents in Systems Engineering

   https://doi.org/10.1115/DETC2018-85941  

   Safarkhani, Salar ; Bilionis, Ilias ; Panchal, Jitesh H. ( August 2018 , ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   Systems engineering processes coordinate the efforts of many individuals to design a complex system. However, the goals of the involved individuals do not necessarily align with the system-level goals. Everyone, including managers, systems engineers, subsystem engineers, component designers, and contractors, is self-interested. It is not currently understood how this discrepancy between organizational and personal goals affects the outcome of complex systems engineering processes. To answer this question, we need a systems engineering theory that accounts for human behavior. Such a theory can be ideally expressed as a dynamic hierarchical network game of incomplete information. The nodes of this network represent individual agents and the edges the transfer of information and incentives. All agents decide independently on how much effort they should devote to a delegated task by maximizing their expected utility; the expectation is over their beliefs about the actions of all other individuals and the moves of nature. An essential component of such a model is the quality function, defined as the map between an agent’s effort and the quality of their job outcome. In the economics literature, the quality function is assumed to be a linear function of effort with additive Gaussian noise. This simplistic assumption ignores two critical factors relevant to systems engineering: (1) the complexity of the design task, and (2) the problem-solving skills of the agent. Systems engineers establish their beliefs about these two factors through years of job experience. In this paper, we encode these beliefs in clear mathematical statements about the form of the quality function. Our approach proceeds in two steps: (1) we construct a generative stochastic model of the delegated task, and (2) we develop a reduced order representation suitable for use in a more extensive game-theoretic model of a systems engineering process. Focusing on the early design stages of a systems engineering process, we model the design task as a function maximization problem and, thus, we associate the systems engineer’s beliefs about the complexity of the task with their beliefs about the complexity of the function being maximized. Furthermore, we associate an agent’s problem solving-skills with the strategy they use to solve the underlying function maximization problem. We identify two agent types: “naïve” (follows a random search strategy) and “skillful” (follows a Bayesian global optimization strategy). Through an extensive simulation study, we show that the assumption of the linear quality function is only valid for small effort levels. In general, the quality function is an increasing, concave function with derivative and curvature that depend on the problem complexity and agent’s skills. 

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3. The Case for Voter-Centered Audits of Search Engines During Political Elections

   https://doi.org/https://doi.org/10.1145/3351095.3372835  

   Mustafaraj, Eni ; Lurie, Emma ; Devine, Claire ( January 2020 , The 3rd ACM Conference on Fairness, Accountability, and Transparency (FAT* 2020))

   Search engines, by ranking a few links ahead of million others based on opaque rules, open themselves up to criticism of bias. Previous research has focused on measuring political bias of search engine algorithms to detect possible search engine manipulation effects on voters or unbalanced ideological representation in search results. Insofar that these concerns are related to the principle of fairness, this notion of fairness can be seen as explicitly oriented toward election candidates or political processes and only implicitly oriented toward the public at large. Thus, we ask the following research question: how should an auditing framework that is explicitly centered on the principle of ensuring and maximizing fairness for the public (i.e., voters) operate? To answer this question, we qualitatively explore four datasets about elections and politics in the United States: 1) a survey of eligible U.S. voters about their information needs ahead of the 2018 U.S. elections, 2) a dataset of biased political phrases used in a large-scale Google audit ahead of the 2018 U.S. election, 3) Google’s “related searches” phrases for two groups of political candidates in the 2018 U.S. election (one group is composed entirely of women), and 4) autocomplete suggestions and result pages for a set of searches on the day of a statewide election in the U.S. state of Virginia in 2019. We find that voters have much broader information needs than the search engine audit literature has accounted for in the past, and that relying on political science theories of voter modeling provides a good starting point for informing the design of voter-centered audits. 

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4. Two for One & One for All: Two-Sided Manipulation in Matching Markets

   https://doi.org/10.24963/ijcai.2022/46  

   Hosseini, Hadi ; Umar, Fatima ; Vaish, Rohit ( July 2022 , Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence Main Track.)

   Strategic behavior in two-sided matching markets has been traditionally studied in a one-sided manipulation setting where the agent who misreports is also the intended beneficiary. Our work investigates two-sided manipulation of the deferred acceptance algorithm where the misreporting agent and the manipulator (or beneficiary) are on different sides. Specifically, we generalize the recently proposed accomplice manipulation model (where a man misreports on behalf of a woman) along two complementary dimensions: (a) the two for one model, with a pair of misreporting agents (man and woman) and a single beneficiary (the misreporting woman), and (b) the one for all model, with one misreporting agent (man) and a coalition of beneficiaries (all women). Our main contribution is to develop polynomial-time algorithms for finding an optimal manipulation in both settings. We obtain these results despite the fact that an optimal one for all strategy fails to be inconspicuous, while it is unclear whether an optimal two for one strategy satisfies the inconspicuousness property. We also study the conditions under which stability of the resulting matching is preserved. Experimentally, we show that two-sided manipulations are more frequently available and offer better quality matches than their one-sided counterparts.

    

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5. Strategic complexity and cognitive skills affect brain response in interactive decision-making

   https://doi.org/10.1038/s41598-022-17951-0  

   Reverberi, Carlo ; Pischedda, Doris ; Mantovani, Marco ; Haynes, John-Dylan ; Rustichini, Aldo ( September 2022 , Scientific Reports)

   Deciding the best action in social settings requires decision-makers to consider their and others’ preferences, since the outcome depends on the actions of both. Numerous empirical investigations have demonstrated variability of behavior across individuals in strategic situations. While prosocial, moral, and emotional factors have been intensively investigated to explain this diversity, neuro-cognitive determinants of strategic decision-making and their relation with intelligence remain mostly unknown. This study presents a new model of the process of strategic decision-making in repeated interactions, first providing a precise measure of the environment’s complexity, and then analyzing how this complexity affects subjects’ performance and neural response. The results confirm the theoretical predictions of the model. The frequency of deviations from optimal behavior is explained by a combination of higher complexity of the strategic environment and cognitive skills of the individuals. Brain response correlates with strategic complexity, but only in the subgroups with higher cognitive skills. Furthermore, neural effects were only observed in a fronto-parietal network typically involved in single-agent tasks (the Multiple Demand Network), thus suggesting that neural processes dealing with cognitively demanding individual tasks also have a central role in interactive decision-making. Our findings contribute to understanding how cognitive factors shape strategic decision-making and may provide the neural pathway of the reported association between strategic sophistication and fluid intelligence.

    

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