Hateful language is a growing problem in digital spaces. Human moderators are not enough to eliminate the problem. Automated hateful language detection systems are used to aid the human moderators. One of the issues with the systems is that their performance can differ depending on who is the target of a hateful text. This project evaluated the performance of the two systems (Perspective and Hatescan) with respect to who is the target of hateful texts. The analysis showed, that the systems performed the worst for texts directed at women and immigrants. The analysis involved tools such as a synthetic dataset based on the HateCheck test suite, as well as wild datasets created from forum data. Improvements to the test suite HateCheck have also been proposed. / Hatiskt språk är ett växande problem i digitala miljöer. Datamängderna är för stora för att enbart hanteras av mänskliga moderatorer. Automatiska system för hatdetektion används därför som stöd. Ett problem med dessa system är att deras prestanda kan variera beroende på vem som är målet för en hatfull text. Det här projektet evaluerade prestandan av de två systemen Perspective och Hatescan med hänsyn till olika mål för hatet. Analysen visade att systemen presterade sämst för texter där hatet riktades mot kvinnor och invandrare. Analysen involverade verktyg som ett syntetiskt dataset baserat på testsviten HateCheck och vilda dataset med texter inhämtade från diskussionsforum på internet. Dessutom har projektet utvecklat förslag på förbättringar till testsviten HateCheck.

Nous présentons une nouvelle approche de préservation de la vie privée pour améliorer l’équité des éléments dans les systèmes de classement. Nous utilisons des techniques de post-traitement dans un environnement de recommandation multipartite afin d’équilibrer l’équité et la protection de la vie privée pour les producteurs et les consommateurs. Notre méthode utilise des serveurs de calcul multipartite sécurisés (MPC) et une confidentialité différentielle (DP) pour maintenir la confidentialité des utilisateurs tout en atténuant l’injustice des éléments sans compromettre l’utilité. Les utilisateurs soumettent leurs données sous forme de partages secrets aux serveurs MPC, et tous les calculs sur ces données restent cryptés. Nous évaluons notre approche à l’aide d’ensembles de données du monde réel, tels qu’Amazon Digital Music, Book Crossing et MovieLens-1M, et analysons les compromis entre confidentialité, équité et utilité. Notre travail encourage une exploration plus approfondie de l’intersection de la confidentialité et de l’équité dans les systèmes de recommandation, jetant les bases de l’intégration d’autres techniques d’amélioration de la confidentialité afin d’optimiser l’exécution et l’évolutivité pour les applications du monde réel. Nous envisageons notre approche comme un tremplin vers des solutions de bout en bout préservant la confidentialité et promouvant l’équité dans des environnements de recommandation multipartites. / We present a novel privacy-preserving approach to enhance item fairness in ranking systems. We employ post-processing techniques in a multi-stakeholder recommendation environment in order to balance fairness and privacy protection for both producers and consumers. Our method utilizes secure multi-party computation (MPC) servers and differential privacy (DP) to maintain user privacy while mitigating item unfairness without compromising utility. Users submit their data as secret shares to MPC servers, and all calculations on this data remain encrypted. We evaluate our approach using real-world datasets, such as Amazon Digital Music, Book Crossing, and MovieLens-1M, and analyze the trade-offs between privacy, fairness, and utility. Our work encourages further exploration of the intersection of privacy and fairness in recommender systems, laying the groundwork for integrating other privacy-enhancing techniques to optimize runtime and scalability for real-world applications. We envision our approach as a stepping stone towards end-to-end privacy-preserving and fairness-promoting solutions in multi-stakeholder recommendation environments.

Pendant la pandémie de Covid-19, les minorités raciales et les groupes économiquement défavorisés ont connu des taux accrus d’infection, d’hospitalisation et de décès dans les zones urbaines. Cette disparité témoigne de l’oppression systématique à laquelle sont confrontées les minorités raciales et la classe ouvrière, qui s’étend évidemment aux services de santé. Les inégalités flagrantes en matière de santé étaient évidentes avant que les vaccins ne soient disponibles, nous ne pouvons donc pas simplement les attribuer à des attitudes culturelles d’hésitation à la vaccination. Dans ce travail, nous présentons des solutions pour optimiser la distribution équitable des vaccins pour différents groupes démographiques, afin de promouvoir un accès équitable aux vaccins lors du premier cycle d’attribution. Nous nous appuyons sur des travaux antérieurs pour construire des réseaux de mobilité de trois zones métropolitaines américaines en utilisant des données de visites réelles dans des lieux publics au cours des premières semaines de la pandémie. Nous proposons une nouvelle méthode utilisant la maximisation de l’influence pour détecter les quartiers les plus influents de la zone urbaine en termes d’efficacité dans la propagation de la maladie. Nous modélisons ensuite la propagation ultérieure de la maladie avec ces quartiers sélectionnés vaccinés. De plus, nous introduisons des considérations d’équité afin de mettre en œuvre un accès équitable aux vaccins pour les groupes raciaux et les groupes de revenus du réseau. Pour fusionner nos solutions avec les stratégies actuelles, nous combinons nos stratégies équitables avec une méthode de priorisation pour les groupes plus âgés du réseau. / During the Covid-19 pandemic, racial minorities and economically-disadvantaged groups experienced heightened rates of infection, hospitalization and death in urban areas. This disparity speaks to the systematic oppression faced by racial minorities and the working classes, which evidently extends to healthcare provisions. The stark inequalities in health outcomes were clear before vaccines became available, so we cannot simply attribute this to cultural attitudes of vaccine hesitancy. In this work, we present solutions to optimize the fair distribution of vaccines for different demographic groups, in order to promote equitable vaccine access in the first round of allocation. We build on previous work to construct mobility networks of three US metropolitan areas using data of real visits to public places during the first weeks of the pandemic. We propose a novel method using influence maximization (IM) to detect the most influential neighborhoods in the urban area in terms of efficacy in spreading the disease. We then model the subsequent disease spread with these selected neighborhoods vaccinated. Additionally, we introduce fairness considerations, to implement equitable vaccine access for racial groups and income groups in the network. To merge our solutions with current strategies, we combine our fair strategies with a prioritization method for older-age groups in the network.

L’équité est un concept large dont la signification et la richesse conceptuelle ont évolué temporellement au sein de la théorie du droit et de la théorie politique. Au travers d’une histoire de l’équité, partant de la conception d’Aristote jusqu’aux juristes réalistes américains, ce mémoire analyse et explique la transformation graduelle de ce concept dans le droit, dont la place fut tantôt au centre des réflexions autour de la justice, tantôt minimisée, voire écartée, mais jamais complètement supprimée. Nous fondant sur cette riche histoire de l’équité, nous reconnectons ce concept phare de la théorie du droit à une réception de celui-ci au sein de la théorie politique. Pour ce faire, nous mobilisons la théorie de la liberté comme non-domination de Philip Pettit comme le cadre d’une pratique contemporaine de l’équité, au travers de la jurisprudence de la Cour suprême du Canada sur l’équité procédurale. / The concept of “equity” is vast, and its meaning and depth have dramatically evolved over time within the political and legal theory fields. This thesis offers a history of the concept of equity, starting from Aristotle’s conception of justice and equity to the American legal realist movement. On that basis, this thesis analyzes and explains the reasons why such a gradual transformation of this concept happened and, therefore, how the role of equity changed from being at the centre of theories of justice and law to being minimized, almost excluded from the sphere of legal theory, but never completely suppressed. Drawing on this rich history of equity, we reconnect this key concept of legal theory with its reception in political theory. In that regard, we offer an account of Philip Pettit’s theory of freedom as non-domination understood as the framework for the contemporary practice of equity, notably through the example of the case law of the Supreme Court of Canada on procedural fairness.

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Local Law Enforcement and Immigration: Lessons and Recommendations from Police Executives (2007-2021) Tonya D. Chapman ABSTRACT The Immigration and Reform Control Act (IRCA) of 1986 and the Illegal Immigration Reform and Immigrant Responsibility Act of 1996 (IIRIRA) authorized the U.S. Immigration and Customs Enforcement agency (ICE) to enter into memoranda of agreement with local law enforcement under section 287(g). The 287(g) program includes the Task Force Model (TFM), Jail Enforcement Model (JEM), Secure Communities (SC), the Priority Enforcement Program (PEP) model and the Warrant Service Officer (WSO) Model, which authorizes specific responsibilities of immigration enforcement to local law enforcement agencies. This dissertation examines the impact of local law enforcement's participation in the various 287(g) programs from the perspective of law enforcement executives. Local law enforcement was granted the responsibility in part because Congress and local elected officials believed that immigration increased crime. However, as of 2022, little research on the nexus between crime and immigration supports that claim; nor does it support the claim that crime rates fell as a result of local law enforcement's participation in the 287(g) programs. Consistent with prior research, this dissertation finds that immigration enforcement has a "null or non-significant" effect on crime in these jurisdictions in comparison to jurisdictions that did not participate in the 287(g) programs. Moreover, this dissertation shows that law enforcement's participation in immigration enforcement led to unintended consequences, including adverse impacts on police legitimacy (trust and fear), perceived crime reporting by immigrant communities, and their community policing efforts. This research provides guidance on best practices to law enforcement in an effort to re-imagine the profession in accordance with procedural justice principles. It examines whether and how immigration enforcement has posed challenges for building trust, legitimacy, community engagement and transparency for law enforcement; looks at whether federal mandates and immigration enforcement affected the advancement of community policing and procedural justice; provides insight on lessons learned from law enforcement's perspective; and contributes to research on the immigration-crime nexus. / Doctor of Philosophy / Local Law Enforcement and Immigration: Lessons and Recommendations from Police Executives (2007-2021) Tonya D. Chapman GENERAL AUDIENCE ABSTRACT Section 287(g) under the Immigration and Reform Control Act (IRCA) of 1986 and the Illegal Immigration Reform and Immigrant Responsibility Act of 1996 (IIRIRA) authorized the U.S. Immigration and Customs Enforcement agency (ICE) to enter into memoranda of agreement with local law enforcement agencies to participate in immigration enforcement. Under Section 287(g), ICE implemented 5 programs, including the Task Force Model (TFM), Jail Enforcement Model (JEM), Secure Communities (SC), the Priority Enforcement Program (PEP) model, and the Warrant Service Officer (WSO) model. This dissertation examines the impact of local law enforcement's participation in the 287(g) programs, from the perspective of law enforcement executives. This dissertation shows that law enforcement's participation in immigration enforcement led to unintended consequences, including adverse impacts on police legitimacy (trust and fear), perceived crime reporting by immigrant communities, and their community policing efforts. The dissertation also finds that immigration enforcement has a "null or non-significant" effect on crime. This research provides guidance on best practices to law enforcement in an effort to re-imagine the profession in accordance with fair and impartial policing principles.

Driven by the surging need for seamless connectivity, research in the wireless communication area has dramatically evolved over the years to meet the increasing demand for data rate and seamless coverage. Such evolvement concurs with a notable increase in data traffic and the widespread of data-hungry devices, thereby inflicting stringent requirements on terrestrial networks. Despite the tremendous advances achieved through the past generations of wireless systems, almost half of the world's population remains unconnected, leading to an accentuated digital divide problem. Therefore, this work invigorates a new connectivity solution that integrates aerial and terrestrial communications with a high-altitude platform station (HAPS) to promote a sustainable connectivity landscape. The connectivity solution adopted in this thesis specifically integrates terrestrial base stations with hot-air balloons under the framework of a cloud-enabled HAPS via a data-sharing fronthauling strategy. The aerial (hot-air balloons) and terrestrial base stations, grouped into disjoint clusters, coordinate their mutual transmission to serve aerial (i.e., drones) and terrestrial users. This work studies the downlink communication from the cloud-enabled HAPS to the aerial and terrestrial users under practical system considerations, namely the limited transmit power and the limited-capacity fronthaul link, per-base station. To this end, the first part of the thesis devises a specific optimization problem that maximizes the network sum-rate while accounting for system design constraints to determine the user association strategy, i.e., user to terrestrial clusters or user to air clusters, and the associated beamforming vectors. The second part of the thesis, then, designs a different resource allocations optimization problem that accounts for the fairness among the users, thus adopting a proportionally fair scheduling scheme to assign users on frequency tones to maximize the log of the long-term average rate. On this account, the work solves a handful of non-convex intricate optimization problems using techniques from optimization theory, namely, fractional programming and $\ell_0$-norm approximation. The work consequently outlines the gains realized by providing on-demand coverage in crowded and unserved areas. Moreover, the thesis illustrates the benefits of coordinating the operations of aerial and terrestrial base stations for interference management, load-balancing, and fairness measures.

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To solve the bottleneck problem of resource utilization and user experience quality in mobile communication networks, 5G introduces network slicing to cope with the huge resource demand of users. To further improve the quality of service for users with different needs, a new fairness definition based on service level objective is introduced. On this basis, a network slicing dynamic resource scheduling strategy based on the greedy algorithm is designed, and the actual application scenarios of slicing scheduling and user scheduling are simplified into a two-layer model, namely the slicing-user model, and combined with the greedy algorithm to make the service weight value. Combine the largest slice and the user with the highest priority, and complete the matching service. The advantage of this method is various system resources can be fairly allocated according to the same proportion to users. Through the optimal combination of each slice and user, the resources of the entire system can be fairly allocated to users with different needs. Python simulation results showed that the newly proposed network slicing dynamic resource scheduling mechanism based on the greedy algorithm can meet the different needs of users and achieve short term fairness, where the users get a fair share of the resource by each missing their SLO by a similar percentage, so as to better meet the needs of users. / För att lösa flaskhalsproblemet med resursanvändning och användarupplevelsekvalitet i mobilkommunikationsnät introducerar 5G nätverksskivning för att klara användarnas enorma resursbehov. För att ytterligare förbättra servicekvaliteten för användare med olika behov införs en ny rättvisedefinition baserad på servicenivåmål. På grundval av detta utformas en dynamisk resursplaneringsstrategi för nätverksskivning baserad på den giriga algoritmen, och de faktiska applikationsscenarierna för skivningsplanering och användarschemaläggning förenklas till en tvåskiktsmodell, nämligen skivningsanvändarmodellen, och kombineras med girig algoritm för att göra tjänstens viktvärde. Kombinera den största delen och användaren med högsta prioritet och slutför motsvarande tjänst. Fördelen med denna metod är att olika systemresurser kan fördelas rättvist enligt samma andel, och genom den bästa kombinationen av varje segment och användare kan hela systemets resurser fördelas rättvist till användare med olika behov. Pythons simuleringsresultat visar att den nyligen föreslagna nätverksskärningsdynamiska resursplaneringsmekanismen baserad på den giriga algoritmen kan tillgodose användarnas olika behov och uppnå kortsiktig rättvisa där användarna får en rättvis andel av resursen genom att var och en saknar sin SLO med en liknande procentsats , för att bättre möta användarnas behov.