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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. 

Social networks arise as a result of complex interactions among people, and homophily plays an important role in this process. If we view homophily as a dominant force in network formation and associate each node with a collection of features, this process gives rise to spatial networks, with likelihood of an edge an increasing function of feature similarity among its incident nodes. A link prediction problem in such spatial networks then amounts to deter- mining whether the pair of nodes are sufficiently close according to this edge likelihood function. We undertake the first algorithmic study of the adversarial side of this problem in which the adversary manipulates features of a subset of nodes on the network to pre- vent predicting target edges. We show that this problem is NP-hard, even if the edge likelihood function is convex. On the other hand, if this function is convex, we show that the problem can be solved with convex programming when the set of nodes that the adversary needs to manipulate is fixed. Furthermore, if the edge likelihood function is linear, we present approximation algorithms for the case when the features are binary, and we wish to hide only a single edge, and for the case when the features are real-valued but we need to hide an arbitrary collection of edges. 

Adversarial machine learning (AML) research is concerned with robustness of machine learning models and algorithms to malicious tampering. Originating at the intersection between machine learning and cybersecurity, AML has come to have broader research appeal, stretching traditional notions of security to include applications of computer vision, natural language processing, and network science. In addition, the problems of strategic classification, algorithmic recourse, and counterfactual explanations have essentially the same core mathematical structure as AML, despite distinct motivations. I give a simplified overview of the central problems in AML, and then discuss both the security-motivated AML domains, and the problems above unrelated to security. These together span a number of important AI subdisciplines, but can all broadly be viewed as concerned with trustworthy AI. My goal is to clarify both the technical connections among these, as well as the substantive differences, suggesting directions for future research. 

The use of algorithmic decision making systems in domains which impact the financial, social, and political well-being of people has created a demand for these to be “fair” under some accepted notion of equity. This demand has in turn inspired a large body of work focused on the development of fair learning algorithms which are then used in lieu of their conventional counterparts. Most analysis of such fair algorithms proceeds from the assumption that the people affected by the algorithmic decisions are represented as immutable feature vectors. However, strategic agents may possess both the ability and the incentive to manipulate this observed feature vector in order to attain a more favorable outcome. We explore the impact that strategic agent behavior can have on group-fair classification. We find that in many settings strategic behavior can lead to fairness reversal, with a conventional classifier exhibiting higher fairness than a classifier trained to satisfy group fairness. Further, we show that fairness reversal occurs as a result of a group- fair classifier becoming more selective, achieving fairness largely by excluding individuals from the advantaged group. In contrast, if group fairness is achieved by the classifier becoming more inclusive, fairness reversal does not occur.