Private Computation of Polynomials over Networks

This study concentrates on preserving privacy in a network of agents where each agent seeks to evaluate a general polynomial function over the private values of her immediate neighbors. We provide an algorithm for the exact evaluation of such functions while preserving privacy of the involved agents. The solution is based on a reformulation of polynomials and adoption of two cryptographic primitives: Paillier as a Partially Homomorphic Encryption scheme and multiplicative-additive secret sharing. The provided algorithm is fully distributed, lightweight in communication, robust to dropout of agents, and can accommodate a wide class of functions. Moreover, system theoretic and secure multi-party conditions guaranteeing the privacy preservation of an agent's private values against a set of colluding agents are established. The theoretical developments are complemented by numerical investigations illustrating the accuracy of the algorithm and the resulting computational cost.Comment: 11 pages, 2 figure

'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions

Data-driven decision-making consequential to individuals raises important questions of accountability and justice. Indeed, European law provides individuals limited rights to 'meaningful information about the logic' behind significant, autonomous decisions such as loan approvals, insurance quotes, and CV filtering. We undertake three experimental studies examining people's perceptions of justice in algorithmic decision-making under different scenarios and explanation styles. Dimensions of justice previously observed in response to human decision-making appear similarly engaged in response to algorithmic decisions. Qualitative analysis identified several concerns and heuristics involved in justice perceptions including arbitrariness, generalisation, and (in)dignity. Quantitative analysis indicates that explanation styles primarily matter to justice perceptions only when subjects are exposed to multiple different styles---under repeated exposure of one style, scenario effects obscure any explanation effects. Our results suggests there may be no 'best' approach to explaining algorithmic decisions, and that reflection on their automated nature both implicates and mitigates justice dimensions.Comment: 14 pages, 3 figures, ACM Conference on Human Factors in Computing Systems (CHI'18), April 21--26, Montreal, Canad

An integrative framework for explaining reactions to decisions: Interactive effects of outcomes and procedures.

The authors suggest that procedural and distributive factors interactively combine to influence individuals' reactions to their encounters with other people, groups, and organizations. Results from 45 independent samples (reviewed herein) show that (a) level of procedural justice is more positively related to individuals' reactions when outcome fairness or valence is relatively low and (b) level of outcome fairness or valence is more positively related to individuals' reactions when procedural justice is relatively low. They present various explanations of the interaction effect. Theoretical progress may be achieved through future efforts to delineate the conditions under which each of the explanations is more versus less likely to account for the interaction. Psychologists have long been interested in explaining individuals' reactions to their encounters with other people, groups, and organizations. Indeed, inquiry into this topic over the past 35 years has consisted of three major waves. The first wave of theory and research dealt with distributive issues, that is, the effects on individuals of the outcomes associated with their relationships or encounters. For example, social exchange theorists suggested that people react more favorably when the valence of the outcomes which they receive is relatively positive (Romans, 1961). Equity theory also provided a prominent outcome-oriented viewpoint. According to equity theorists, distributive justice-defined as the perceived proportion of individuals' inputs into and outcomes derived from the relationship in comparison with the inputs and outcomes of relevant others-shapes individuals' work motivation and job satisfaction The second wave of theory and research revealed that individuals' reactions also depend on the fairness of the procedures used by the other party to plan and implement resource allocation decisions. Most notably

维护工作中的公平 增强员工承诺

员工与顶头上司有好的关系是其工作幸福感的重要来源之一。上司的决策从根本上塑造着员工对其未来工作关系的期望:不仅包括他的上司将会做什么,还包括他们将会如何执行其决策。因此,双方的信任关系就至关重要。但是,对许多员工来说,与其顶头上司的互动可以说是最令其不快的工作内容之一

Crossing Ethical Boundaries in the Pursuit of Passion: How Passion Gaps Cause Unethical Pro-Organizational Behavior

Many contemporary organizations encourage their employees' pursuit of passion for work. However, we propose that this strategy may simultaneously increase the likelihood their employees engage in unethical pro-organizational behavior (UPB), immoral acts that benefit the company. Specifically, we suggest that when employees fall short of desired levels of work passion-i.e., when they experience a "passion gap"-their sense of self is threatened. One way employees deal with the self-threat elicited by passion gaps is by engaging in UPB, which gives them the feeling that they are worthy organizational members. Eight studies (N = 2,695)-including two field studies, an experimental-causal-chain analysis, and two intervention studies-provide support for the proposed relationships between passion gaps, self-threat, and the tendency to engage in UPB. Two interventions highlight factors that directly attenuate the self-threat prompted by passion gaps: (1) having employees engage in self-affirmation, and (2) de-emphasizing the role of passion in predicting success. Our results suggest that organizations' increased emphasis on the pursuit of passion may have an unintended consequence: it leads those employees who fall short of desired levels of passion to engage in unethical behavior designed to help the company, which may harm the organization in the long-run