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We introduce a new, group fairness-inspired stochastic multi-armed bandit problem in the pure exploration setting. We look at the discrepancy between an arm’s mean reward from a group and the highest mean reward for any arm from that group, and call this the disappointment that group suffers from that arm. We define the optimal arm to be the one that minimizes the maximum disappointment over all groups. This optimal arm addresses one problem with maximin fairness, where the group used to choose the maximin best arm suffers little disappointment regardless of what arm is picked, but another group suffers significantly more disappointment by picking that arm as the best one. The challenge of this problem is that the highest mean reward for a group and the arm that gives that reward are unknown. This means we need to pull arms for multiple goals: to find the optimal arm, and to estimate the highest mean reward of certain groups. This leads to the new adaptive sampling algorithm for best arm identification in the fixed confidence setting called MD-LUCB, or Minimax Disappointment LUCB. We prove bounds on MD-LUCB’s sample complexity and then study its performance with empirical simulations. / Graduate

Yes / The concept of ‘fairness’ has been used to frame political struggles by politicians and activists across the political spectrum. This article looks at its use in the US State of Wisconsin during the ‘Uprising’ – a series of occupations, protests, recall elections and militant direct action that began in 2011. These events were a response to a ‘budget repair bill’ that sought to strip public sector union members of their collective bargaining rights and to apply severe austerity measures within the State. This article suggests that although ‘fairness’ has a certain broad-based and intuitive appeal, its mutability means that it is unlikely to be successful in framing a structural critique that can build and sustain social action. Instead, it argues that framing this conflict as an uprising suggested a more explicit form of resistance that enabled a wider mobilization, and this can best be understood as an example of social (movement) unionism – the extension of traditional work place rights approaches to include broader agendas of social justice, civil rights, immigrant rights and economic justice for non-unionized workers.

The purpose of the current study was to investigate whether the separate contributors to procedural and distributive justice also affected the other form of justice. Previous research investigating these cross over effects of justice contributors had not examined inputs in addition to outcomes and procedures, and had typically assumed outcome level to be equivalent to the equitableness of outcomes. Subjects were 120 undergraduate psychology students. Outcomes, inputs and procedures were manipulated in a laboratory experiment in order to assess their independent and combined effects on distributive and procedural justice perceptions. In contrast to past research, the current study found a weak and inconsistent effect of procedures on distributive justice perceptions. Outcome level had a strong effect on both procedural and distributive justice perceptions. In addition, outcome fairness was found to effect procedural justice perceptions. When procedures were fair, the equitableness of outcomes influenced distributive justice ratings. When procedures were unfair, however, the equitableness of outcomes did not influence distributive justice judgements. Implications for procedural justice conceptualizations, equity theory and organizations are discussed. / Ph. D.

In organizations, it is inevitable that some business activities might seem unfair to subordinates. Social accounts—the explanations managers give their subordinates for those decisions—are known to be a useful tool for managing subordinates’ fairness concerns. Over three decades of research, we learn that social accounts are effectiveness in improving subordinates’ fairness perceptions and reducing their negative reactions. Yet, we have only limited understanding about how social accounts affect the perceptions and behaviors of managers—those who construct and give them. The purpose of this dissertation is to examine the extent to which constructing accounts affects account-givers’ perceptions and behaviors. Drawing on research in social account and behavioral ethics, a model was developed to test the positive effect of constructing accounts on unethical behavior (direct effect) through moral disengagement and guilt (indirect effect). In respect to account types, it was hypothesized that constructing justifications would lead to higher moral disengagement, less guilt, and more unethical behavior, compared with constructing excuses. Account feedback was hypothesized to moderate the indirect effects of justifications and excuses on unethical behaviors such that account acceptance would strengthen moral disengagement and weaken guilt, and in turn, increase unethical behavior. Two experimental designed studies were conducted to test these hypotheses. In Study 1, utilizing a sample of 128 management students, constructing accounts was found to have a positive effect on unethical behavior (i.e., nepotism) with guilt but not moral disengagement explaining some of the variances in this relation. In contrast to my hypotheses, constructing excuses was found to increase guilt more than constructing justifications. Using a sample of 136 management students, Study 2 generally replicated the results found in Study 1: constructing accounts was found to increase unethical behavior (i.e., dishonesty) through guilt, with excuses having a greater effect. This dissertation concludes with a discussion on contributions, practical implications, limitations, and the direction for future research on social accounts and behavioral ethics. / Ph. D.

The present field experiment investigated the interaction between influence and locus of control in determining procedural justice and satisfaction, in a classroom situation. Perceptions of influence accounted for unique variance in procedural justice and satisfaction. The proposed moderating effects of locus of control on the influence-procedural justice and influence-satisfaction relationships were not supported. Unfortunately, the manipulation of influence was unsuccessful, and several methodological considerations are proposed for future research. / Master of Science

The field of Human Resource Management (HRM) has experienced a significant transformation with the emergence of big data and algorithms. Major technology companies have introduced software and platforms for analyzing various HRM practices, such as hiring, compensation, employee engagement, and turnover management, utilizing algorithmic approaches. However, scholarly research has taken a cautious stance, questioning the strategic value and causal inference basis of these tools, while also raising concerns about bias, discrimination, and ethical issues in the applications of algorithms. Despite these concerns, algorithmic management has gained prominence in large organizations, shaping workforce management practices. This thesis aims to address the gap between the rapidly changing market of HRM algorithms and the lack of theoretical understanding. The thesis begins by conducting a comprehensive review of HRM algorithms in HRM practice and scholarship, clarifying their definition, exploring their unique features, and identifying specific topics and research questions in the field. It aims to bridge the gap between academia and practice to enhance the understanding and utilization of algorithms in HRM. I then explore the legal, causal, and moral issues associated with HR algorithms, comparing fairness criteria and advocating for the use of causal modeling to evaluate algorithmic fairness. The multifaceted nature of fairness is illustrated and practical strategies for enhancing justice perceptions and incorporating fairness into HR algorithms are proposed. Finally, the thesis adopts an artifact-centric approach to examine the ethical implications of HRM algorithms. It explores competing views on moral responsibility, introduces the concept of "ethical affordances," and analyzes the distribution of moral responsibility based on different types of ethical affordances. The paper provides a framework for analyzing and assigning moral responsibility to stakeholders involved in the design, use, and regulation of HRM algorithms. Together, these papers contribute to the understanding of algorithms in HRM by addressing the research-practice gap, exploring fairness and accountability issues, and investigating the ethical implications. They offer theoretical insights, practical recommendations, and future research directions for both researchers and practitioners. / Thesis / Doctor of Philosophy (PhD) / This thesis explores the use of advanced algorithms in Human Resource Management (HRM) and how they affect decision-making in organizations. With the rise of big data and powerful algorithms, companies can analyze various HR practices like hiring, compensation, and employee engagement. However, there are concerns about biases and ethical issues in algorithmic decision-making. This research examines the benefits and challenges of HRM algorithms and suggests ways to ensure fairness and ethical considerations in their design and application. By bridging the gap between theory and practice, this thesis provides insights into the responsible use of algorithms in HRM. The findings of this research can help organizations make better decisions while maintaining fairness and upholding ethical standards in HR practices.