When people must share things, what does it mean to share fairly? Do all people around the world have the same idea of what is fair or unfair? Are humans born with a feeling about what is fair and unfair, or is it something we learn as we grow up? Scientists study how people from different cultures choose to share things in various situations, and whether people think different ways of sharing are fair or unfair. The article describes an experiment in which scientists studied whether children from different cultures have different ideas about what is fair. These studies are important for understanding how humans are similar and different from each other and from other animals, and they also help us understand how we can work to create a world that is considered fair by everyone.

L’automatisation de la prise de décision dans des applications qui affectent directement la qualité de vie des individus grâce aux algorithmes de réseaux de neurones est devenue monnaie courante. Ce mémoire porte sur les enjeux d’équité individuelle qui surviennent lors de la vérification, de l’entraînement et de la prédiction des réseaux de neurones. Une approche populaire pour garantir l’équité consiste à traduire une notion d’équité en contraintes sur les paramètres du modèle. Néanmoins, cette approche ne garantit pas toujours des prédictions équitables des modèles de réseaux de neurones entraînés. Pour relever ce défi, nous avons développé une technique de post-traitement guidée par les contre-exemples afin de faire respecter des contraintes d’équité lors de la prédiction. Contrairement aux travaux antérieurs qui ne garantissent l’équité qu’aux points entourant les données de test ou d’entraînement, nous sommes en mesure de garantir l’équité sur tous les points du domaine. En outre, nous proposons une technique de prétraitement qui repose sur l’utilisation de l’équité comme biais inductif. Cette technique consiste à incorporer itérativement des contre-exemples plus équitables dans le processus d’apprentissage à travers la fonction de perte. Les techniques que nous avons développé ont été implémentées dans un outil appelé FETA. Une évaluation empirique sur des données réelles indique que FETA est non seulement capable de garantir l’équité au moment de la prédiction, mais aussi d’entraîner des modèles précis plus équitables. / Algorithmic decision-making driven by neural networks has become very prominent in applications that directly affect people’s quality of life. This paper focuses on the problem of ensuring individual fairness in neural network models during verification, training, and prediction. A popular approach for enforcing fairness is to translate a fairness notion into constraints over the parameters of the model. However, such a translation does not always guarantee fair predictions of the trained neural network model. To address this challenge, we develop a counterexample-guided post-processing technique to provably enforce fairness constraints at prediction time. Contrary to prior work that enforces fairness only on points around test or train data, we are able to enforce and guarantee fairness on all points in the domain. Additionally, we propose a counterexample guided loss as an in-processing technique to use fairness as an inductive bias by iteratively incorporating fairness counterexamples in the learning process. We have implemented these techniques in a tool called FETA. Empirical evaluation on real-world datasets indicates that FETA is not only able to guarantee fairness on-the-fly at prediction time but also is able to train accurate models exhibiting a much higher degree of individual fairness.

Despite repeated attempts by school administrators, policymakers and researchers to diagnose and correct rising occurrences of teacher turnover, there has been little change in the actual efforts to retain teachers in academy organizations. In response, this study was conducted to describe process constraints within the academy organization that are responsible for managing teacher turnover. To provide a description of current teacher retention efforts by school administrators, a survey instrument was administered to school teachers in the State of Florida. The population sample was dispersed throughout the entire state and closely reflected the demographics of Florida school teachers. The survey addressed two issues: Whether or not there is a consistent effort by school administrators to gauge a teacher's desire to remain in their current position and whether or not the teachers perceive related administration decisions to be fair. To describe perceptions of fairness, a two-prong model was used to measure perceptions of Voice and Equity. Of the 215 respondents, only about 25 percent were administered a survey within the last year that gauges their desire to remain in their current position. Of these respondents who were given a retention survey by their administrators, results were mixed, with only about half of all respondents leaning towards a favorable perception of fairness. The results indicate that there is a logical need for process improvement within the administration of academy organizations before teacher turnover could be managed effectively.

Workplace arrogance has emerged as a research focus area for many industrial-organizational psychologists. Employees who demonstrate arrogance tend to demonstrate poor job performance, executive failure and poor overall organizational success. The present study investigates arrogance measured by the Workplace Arrogance Scale (WARS: Johnson et al., 2010) in relation to the Honesty Humility facet of the HEXACO Personality Index-Revised (HEXACO PI-R: LEE & Ashton, 2004). A total of 273 participants completed the WARS and HEXACO PI-R Honesty-Humility Facet of the HEXACO. Results show significant, strong negative correlations between the Honesty-Humility subfacets and the overall Honesty Humility facet score with the WARS scores. These findings indicate that workers high in arrogance lack important honesty-humility characteristics. Once we fully understand the complex mixture of personality traits that make up workplace arrogance, we can begin to screen for it in the hiring process and develop ways to better address it in the workplace.

In professional partnerships, partners have to agree how to split their income between each other. Such a profit sharing system (PSS) must be perceived as being fair and motivating to ensure the enduring success of the partnership. Surprisingly, quite different systems are in use today in otherwise comparable firms. The understanding of a “fair share” and how to motivate best varies con-siderably. Existing literature on professional service firms rarely discusses in which circumstances the different PSS types are adequate; non-economic per-spectives are scarce. Using semi-structured interviews with senior partners from large German law firms, this study evaluates their understanding of trust, fairness and motivation, and how that links to their respective PSS’s. It adds the otherwise missing peer-to-peer perspective to existing organisational research on fairness, trust and motivation. The findings include the presence of both extrinsic and intrinsic motivation through money, but also through peer pressure. Different fairness ideals clearly link to PSS types. Mutual trust, based on knowing each other, is key in all but one PSS type. An important, but yet overlooked differentiator between PSS’s is whether profit distribution decisions are made based on algorithms or on human (committee) decisions. A new framework is developed that links the beliefs and values of the partners with the specific characteristics of the PSS, which are systematically assessed for the first time. This framework offers partners from law firms and potentially other professional service firms a methodical approach to identify and discuss their needs and to identify the most appropriate PSS for their specific situation.

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<p>In this thesis we study radio resource management (RRM) in two types of CDMA-based wireless networks, cognitive radio networks (CRNs) and cooperative communication networks. In the networks, all simultaneous transmissions share the same spectrum and interfere with one another. Therefore, managing the transmission power is very important as it determines other aspects of the network resource allocations, such as transmission time and rate allocations. The main objective of the RRM is to efficiently utilize the available network resources for providing the mobile users with satisfactory quality of service (QoS).</p> / Doctor of Philosophy (PhD)