Saudi Cultural Missions Theses & Dissertations
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Item Restricted Fair and Accurate Machine Learning in Dynamic and Multi-domain Settings(Rutgers, The State University of New Jersey, 2024-05-01) Almuzaini, Abdulaziz; Singh, Vivek; Pennock, DavidA multitude of decision-making tasks, such as content moderation, medical diagnosis, misinformation detection, and recidivism prediction, are increasingly being automated due to recent developments in machine learning (ML). While ML models demonstrate superior capabilities in large-scale data processing and complex pattern recognition compared to humans, the decisions they make can profoundly impact individuals' opportunities and lives, necessitating the assurance of their accuracy and fairness. Besides developing ML models in controlled lab environments, automated machine learning tasks are often used in real-world settings where the concept of stationarity (i.e., the independent and identically distributed i.i.d. assumption) is frequently violated, leading to a notable decrease in the effectiveness of machine learning models. Specifically, real-world ML models can be trained on particular domains and deployed in dissimilar domains. These domains encompass diverse time points, heterogeneous population groups, or disparate tasks demanding careful, dynamic, ethical, and knowledge-transferring model development techniques. Due to the dynamic nature of many machine learning tasks and their continuous evolution, a previously trained model may become unfair or erroneous over time. Additionally, machine learning applications can be particularly challenging due to limited data or computational resources, which often require developers to leverage knowledge from other domains. In this dissertation, we investigate these issues and suggest ways to mitigate the challenges of maintaining the goals of fairness and accuracy in dynamic and multi-domain settings. Particularly, to mitigate the impact of the dynamic issue, we present a pair of anticipatory bias correction techniques that target fairness and accuracy simultaneously in temporally shifting and delayed labeling contexts, supporting the goals of timely and safe model adaptation. Furthermore, we leverage transfer learning methodology to study ML performance in developing a fair and accurate dermatological image processing task for skin cancer diagnosis using datasets gathered from various domains (i.e., locations) and models trained on different contexts (i.e., pre-trained image recognition model). Lastly, we explore the feasibility of combining diverse commercial pre-trained black-box models developed in various domains to jointly enhance fairness and accuracy for a sentiment analysis task. We present an overview of the observed results for each work, discuss the identified limitations, and propose future research directions. These results represent significant progress toward developing fair and accurate ML algorithms in dynamic and multi-domain settings.29 0Item Restricted The Responsibility of Non-Profit Organisations in Saudi Arabia for Terrorism Financing with Reference to Law and Practice in England and Wales.(University of Leeds, 2024-03-05) Alsalmi, Mohammad Abdulrahman; Walker, Clive; O'Reilly, ConorThe Kingdom of Saudi Arabia's (KSA) non-profit (NPO) sector has been implicated in allegations of providing funding to radical Islamic organizations. This association, voiced especially after 9/11, has raised critical questions about the KSA's understanding of, and response to, this risk. This thesis undertakes a comprehensive investigation into the policies, laws, and practices employed within the country to combat terrorism financing (CTF) through NPOs. Beyond assessing the effectiveness of these measures, this research delves into the fairness of the KSA's CTF approach towards NPOs. Addressing these core questions through documentary analysis and fieldwork, the thesis explores the deficiencies and barriers, not only legal but also political, cultural, and structural which hinder the KSA in effectively and fairly countering terrorism financing through NPOs. One of the significant identified factors pertains to the incorporation of Sharia within the KSA's legal system and the inherent challenges it poses for the implementation of CTF procedures. Sharia principles do not explicitly address CTF-related matters and also affect the impact of more recent legislation against CTF. Relying on a legal framework that prioritizes historical norms over contemporary legal requisites can impede efforts, whether in criminal law, civil law, or regulatory action in the treatment of the NPO sector. In addition to identifying the factors contributing to the shortcomings in the KSA's CTF approach involving NPOs, this thesis offers recommendations for reforming the KSA's NPO governance system having regard to notions of responsive regulation and an assemblage model. These recommendations draw insights from the governance experiences of England and Wales which form the basis for policy transfer.25 0