Thesis (Ph. D.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

Thesis (Ph.D.)--University of Nebraska-Lincoln, 2006. / Title from title screen (site viewed June 8, 2007). PDF text: 141 p. : col. ill. ; 0.80Mb. UMI publication number: AAT 3239362. Includes bibliographical references. Also available in paper, microfilm and microfiche formats.

Universiẗat, Diss., 2003--Bremen. / Erscheinungsjahr an der Haupttitelstelle: 2002.

The use of consumer credit background checks in employee selection has been increasing and, in recent years, has been the topic of heated debate. Supporters and opponents contest the benefits and consequences of the use of credit background checks for personnel selection, with arguments on both sides predominantly based on anecdotal evidence; empirical research is missing from the debate. The lack of research to support these arguments is concerning due to the historical, evidence-based, relationship between employee selection and organizational justice. Job applicants pursue employment to fulfill economic and socio-economic needs and expect fair processes and outcomes. Imbalances in the input-to-output ratio have been suggested to result in behavioral outcomes intent to restore balance. Two experimental studies examined justice-related consequences of the use of ECCs in personnel selection. Study 1 examined potential applicants' perceptions of organizational justice as well as their engagement in both pro-social (organizational citizenship behaviors-OCB) and anti-social (counterproductive workplace behavior-CWB) behavior as a result of failing a job selection hurdle on the basis of a poor ECC outcome (in comparison to a standard personnel selection criteria- job qualifications and work experience). A sample of adults (N = 171) was recruited from Amazon MTurk to ostensibly pilot test an online employee selection battery. They were randomly assigned to either pass both the ECC and Job Qualifications/Experience tests or to fail one or the other (thus being dropped from further consideration). Applicants denied employment based on their consumer credit experienced significantly lower distributive and procedural justice. They were also more likely than those denied employment on the basis of qualifications and experience and those passing both assessments to engage in a CWB. There were no effects on OCB. The effect of failing on the basis of ECC on CWB engagement was mediated by justice perceptions. Study 2 examined how applicants with weak credit, in comparison to applicants with weak qualifications/experience are perceived by raters. Study 2 also examined the potential for disparate treatment against minority applicants on the basis of ECCs - an issue of distributive justice. A similar sample of (N = 155) working adults recruited from Amazon MTurk were asked to make personnel selection judgments of applicants who varied by type of Applicant Credential (weak consumer credit history but strong job qualifications and experience; or weak job qualifications and experience but strong consumer credit history) and race (White/Black). Type of Applicant Credential significantly affected employability ratings such that those with weak qualifications and experience but strong credit were rated as less employable than those with strong qualifications/experience but weak credit. Also, applicants with weak credit (but strong qualifications/experience) were perceived as more likely to exhibit behavioral indicators of fraud than applicants with weak qualifications/experience (but strong credit). Race of the applicant did not moderate these effects. These studies provide evidence of both individual, and organizational, level outcomes associated with the use of ECCs as well as potential retaliatory behavior (CWB) directed at the organization from applicants denied employment based on credit. However, the findings also suggest that ECCs are not prone to race discrimination effects. The findings fill a necessary gap in the research literature by providing empirical evidence directly related to the use of consumer credit in selection.

Positioning itself within policy debates on the best way to deal with disputes in UK workplaces and the (potential) resultant increased interest in mediation, this thesis draws on literature from law, philosophy, psychology and management to add to the growing, but largely theoretically-underdeveloped research on workplace mediation. In this research, mediation refers to a voluntary and confidential process where parties to dispute seek a mutually agreed outcome. This process is facilitated by an impartial third-party mediator. The research offers an empirically-informed theoretical framework exploring the extent to which the use of mediation to deal with bullying and harassment is appropriate. In asking whether mediation is appropriate, it argues that it is necessary to consider whether its use is not only feasible but also fair. Using Rawls’s (2001) theory of justice as fairness to structure the discussion and focusing on cases involving sex, race and sexual orientation it constructs an argument for the use of fairness as a guiding concern for an understanding of mediation grounded in an appreciation of public values and notions of social cooperation. It explores tensions between the nature of mediation and of bullying and harassment to question the extent to which an emphasis on cost/efficiency and empowerment in mediation rhetoric may obscure questions of the privatisation and individualisation of systemic and structural problems. Within this discussion theoretical and practical questions are identified and are then explored through the use of a mixed method research design comprised of a small-scale questionnaire (N=108), interviews (N=20) and focus groups (Four groups, N=16). Samples were purposively recruited and consisted of those over 18 years old with six month’s work experience in a UK workplace (questionnaire/focus groups) and external workplace mediators (interviews). Answers to the questions are offered in the form of a framework comprised of a theoretical model and a practically-orientated schematic. It is argued that the reconciliation of potential conflicts between mediation and bullying and harassment are found in a greater understanding of the way mediation operates in practice. This understanding is guided by an appreciation that different standards of reasonableness apply to different behaviours and that individuals, organisations and the courts have differing levels of responsibility for setting and upholding these standards. In meeting this responsibility it is important an organisation is seen as a party to the mediation process since a threat to fairness arises not from privatisation per se but from a personalisation of problems of organisational and/or societal significance. Rather than reject the use of mediation in such situations it suggests the notion of ‘tailored privatisation’ offering a compromise between the concerns of privatisation and the purported benefits of mediation.

Video streaming over the best-effort networks is a challenging problem due to the time-varying and uncertain characteristics of the links. When multiple video streams are present in a network, they share and compete for the common bandwidth. In such a setting, a bandwidth allocation algorithm is required to distribute the available resources among the streams in a fair and efficient way. Specifically, it is desired to establish fairness across end-users' Quality of Experience (QoE). In this research, we propose three novel methods to provide QoE-fair network bandwidth allocation among multiple video streaming sessions. First, we formulate the problem of bandwidth allocation for video flows in the context of Network Utility Maximization (NUM) framework, using sigmoidal utility functions, rather than conventional but unrealistic concave functions. An approximation algorithm for Sigmoidal Programming (SP) is utilized to solve the resulting nonconvex optimization problem, called NUM-SP. Simulation results indicate improvements of at least 60% in average utility/QoE and 45% in fairness, while using slightly less network resources, compared to two representative methods. Subsequently, we take a collaborative decision-theoretic approach to the problem of rate adaptation among multiple video streaming sessions, and design a multi-objective foresighted optimization model for network resource allocation. A social welfare function is constructed to capture both fairness and efficiency objectives at the same time. Then, assuming a common altruistic goal for all network users, we use multi-agent decision processes to find the optimal policies for all players. We propose a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) model for the conventional IP networks and a Multi-agent Markov Decision Process (MMDP) model for the SDN-enabled wireless networks. By planning these cooperative decision process models, we find the optimal network bandwidth allocation that leads to social welfare maximization. Distributed multi-agent reinforcement learning algorithms are also designed and proposed as a low-complexity model-free solution to these optimization problems. Simulations of the proposed methods show that the resulting optimal policies of the novel Social Utility Maximization (SUM) framework outperform existing approaches in terms of both efficiency and fairness. The Dec-POMDP model applied to a server-side rate adaptation results in 25% improvement in efficiency and 13% improvement in fairness, compared to one popular protocol of congestion control for multimedia streaming. Our performance evaluations also show that the MMDP model applied to a client-side rate adaptation like DASH improves efficiency, fairness, and social welfare by as much as 18%, 24%, and 25%, respectively compared to current state-of-the-art.

Thesis (LL.D.)--University of Leiden / "Between legality and illegality: An inquiry into the doctrine of encroachment of rights and powers as a result of the corrective action of reasonableness and fairness in law with particular reference to the encroachment of ownership of immovable property" ...refer to full-text for more details which includes abstracts Afrikaans. Text in Afrikaans.

Algorithms that are being used in sensitive contexts such as deciding to give a job offer or giving inmates parole should be accurate as well as being non-discriminatory. The latter is important especially due to emerging concerns about automatic decision making being unfair to individuals belonging to certain groups. The machine learning literature has seen a rapid evolution in research on this topic. In this thesis, we study various problems in sequential decision making motivated by challenges in algorithmic fairness. As part of this thesis, we modify the fundamental framework of prediction with expert advice. We assume a learning agent is making decisions using the advice provided by a set of experts while this set can shrink. In other words, experts can become unavailable due to scenarios such as emerging anti-discriminatory laws prohibiting the learner from using experts detected to be unfair. We provide efficient algorithms for this setup, as well as a detailed analysis of the optimality of them. Later we explore a problem concerned with providing any-time fairness guarantees using the well-known exponential weights algorithm, which leads to an open question about a lower bound on the cumulative loss of exponential weights algorithm. Finally, we introduce a novel fairness notion for supervised learning tasks motivated by the concept of envy-freeness. We show how this notion might bypass certain issues of existing fairness notions such as equalized odds. We provide solutions for a simplified version of this problem and insights to deal with further challenges that arise by adopting this notion. / Graduate

Chip Multi-Processor (CMP) platforms, well-established in the server, desktop and embedded domain, succeeded in overcoming the power consumption and heat dissipation bottlenecks by integrating multiple cores, less complex and powerful than their single-core ancestors, in a single die. A major issue induced by the design of the CMPs is contention for the shared resources of the platform, Last Level Cache (LLC) and main memory bandwidth. Applications, running concurrently on the cores, compete with each other for the shared resources, and are subject to performance degradation. The way applications are assigned to the CMP, is crucial for the overall performance of the system. A scheduling policy that accounts for contention will bring high performance speed-ups, whereas an agnostic one will generate unpredictable contention conditions. For this reason the significance of the scheduler has been elevated, as it is the component that determines which applications utilize the resources each time period.In this thesis, we address cross-core interference on CMP platforms, by designing scheduling policies that improve performance and fairness. We deal with contention in three ways. In our first approach, we incorporate the notion of progress in order to balance unfairness among the applications of the workload. Performance degradation is not evenly distributed and progress greatly varies among them. In order to provide a fair execution environment, we monitor, at run-time, applications assigned to the CPU and prioritize them based on the extent at which they are affected by contention.In our second approach, we target performance by mitigating contention on shared resources. It is necessary to decide, out of all the possible application schedules, the one that generates the least amount of resource interference. To achieve that, the first indispensable step is to extract an interference profile for the applications executed on the CMP. We accomplish that by applying pressure to all levels of memory hierarchy and identifying the point at which performance is compromised. From our analysis, we understand that shared resources can tolerate pressure of certain amount; applications can be grouped together if the overall generated pressure does not reach the saturation point of the shared resources. Having extracted this information, we proceed to the placement of the application in such a way that overall resource requirements are as balanced as possible across the execution.Finally, we design a policy in order to improve performance and fairness at the same time. Applications that heavily rely on the LLC are separated from those with high main memory bandwidth, in order to avoid the destructive effects caused by the LLC thrashing behavior of the latter. The group executed on the CPU is determined based on the key observation that the overall requirements of the group should not exceed the saturation limits of the CMP. Additionally, during execution, the progress for each application is estimated and those with the least accumulated progress are prioritized.Our proposed policies are evaluated in an Intel Xeon E5-2620 v3 processor. A variety of benchmark suites were utilized to generate mixes of diverse characteristics. Our methodologies are implemented in user-space and can be deployed on Linux-based systems. Experimental results show the benefits of tackling contention in shared resources. We achieve throughput gains of up to 16% and unfairness is reduced by 2.37x on average compared to Linux scheduler.

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