Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 23 for “"Responsible AI"”.
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RESPONSIBLE AI GOVERNANCE: BALANCING THE GOALS OF BIAS MITIGATION AND PRIVACY PROTECTION THROUGH TRANSPARENCY
… societal reliance on artificial intelligence (AI), this thesis begins by establishing a foundational understanding of AI systems, their capabilities, applications, and social implications, before turning to its central focus: the interplay between the goals of debiasing AI systems and …
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Responsible AI : the praxis of AI and data protection management : negotiating innovation and FAT principles
… and the implications for organisations managing AI technologies are particularly significant. Whereas much research focuses on algorithmic biases and the development of AI, this research explores other important concerns arising from the uses of personal data during the introduction of AI, which …
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Characterizing Algorithmic Performance in Machine Learning for Education
The integration of artificial intelligence (AI) in educational systems has revolutionized the field of education, offering numerous benefits such as personalized learning, intelligent tutoring, and data-driven insights. However, alongside this progress, concerns have arisen about potential …
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Back to the building blocks: Making forecasting more context aware through human and data-centric practices
The rise of artificial intelligence (AI) and machine learning (ML) presents substantial opportunities for high-stakes fields like health and public policy. While model-centric AI, focusing on algorithmic improvements and architectural innovations, has historically dominated the field, growing calls …
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Engineering data-sharing practices for a fair and trustworthy AI
… that ML applications are more likely to fail in identifying women than males in hospitals. Recent research has identified the data used to train these models as one of the causes of these issues. The research community has proposed guidelines to detect the dimensions that can generate …
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Private, Verifiable, and Auditable AI Systems
… verifiability, and auditability in modern AI, particularly in foundation models. It argues that technical solutions that integrate these elements are critical for responsible AI innovation. Drawing from international policy contributions and technical research to identify key risks in the …
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Methods for mitigating bias of biometric systems
… frequently display significant biases against specific demographic groups. This inherent unfairness compromises the integrity and equity of algorithmic decision-making processes relying on these systems. While mitigating such bias is critically important for responsible AI deployment, …
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Aligning AI with Human Values: A Path Towards Trustworthy Machine Learning Systems
… However, as artificial intelligence (AI) technologies advance and become more deeply integrated into daily life, they also introduce risks such as malicious exploitation, misinformation, and unfair decision-making, which can undermine their reliability and ethical integrity. Given AI’s …
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Examining large language models for safety and robustness through the lens of social science
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms
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Towards Human-AI Teaming for Skill Development: From Dyadic Interview Practice to Triadic Programming Collaboration
… has become critical. This thesis explores how AI can support skill development in these domains, progressing from AI as a sole practice partner to AI as a collaborative teammate. Our first two studies investigate LLM-based conversational AI for interview preparation. Study 1 developed an …
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How Large Language Models Are Reshaping Skills and Job Requirements for Public Health Professionals in Saudi Arabia
… decision-making. Yet, their integration raises concerns about workforce preparedness, evolving skill requirements, and ethical oversight. In Saudi Arabia, where Vision 2030 prioritizes digital transformation in healthcare, understanding how public health professionals adapt to these …
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Analysis and Chemical Applications of Metal–Metal Bonds and Large Language Models
… and emerging technologies. Yet challenges remain in tuning metal–metal interactions, stabilizing reactive battery interfaces, and improving access to data interpretation in chemical research and education. This work addresses these challenges through a combination of synthetic chemistry, …
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Safeguarding sensitive data: prompt engineering for Gen AI
Generative Artificial Intelligence (GenAI) represents a transformative advancement in technology with capabilities to autonomously generate diverse content, such as text, images, simulations, and beyond. While GenAI offers significant operational benefits it also introduces risks, particularly in …
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Order-Leading Branch and Bound for Neural Network Verification
… Although high-level principles for safe and responsible AI are now widely endorsed, existing techniques that analyze each model in isolation often fall short, facing opaque behaviors, cascading errors across continual retraining, pruning, or unlearning throughout the lifecycle. This thesis …
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Sensitive Attribute Association Bias in Latent Factor Recommendation Algorithms: Theory and In Practice
… model and implicit attributes into the trained latent space. This type of bias occurs when entity embeddings showcase significant levels of association with specific types of explicit or implicit entity attributes, thus having the potential to introduce representative harms for both …
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Designing Meaningful Algorithmic System Transparency for Non-Expert Users
… transparency needs deserve urgent attention as daily lives increasingly rely on such systems. In this thesis, I explore how to design meaningful algorithmic system transparency for non-expert users. I highlight three core barriers to transparency, among other challenges raised in the literature. …
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Ethical Analytics: A Framework for a Practically-Oriented Sub-Discipline of AI Ethics
… a new, practically-oriented sub-discipline of AI Ethics by outlining needs, highlighting shortcomings in current approaches, and providing a framework for Ethical Analytics, a field concerned with the study of the ethical issues surrounding the development, deployment, and/or dissemination of …
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Trustworthy Machine Learning: From Algorithmic Transparency to Decision Support
… Algorithmic transparency tools, such as explainability and uncertainty estimates, demonstrate the trustworthiness of a model to a decision-maker. In this thesis, we first explore how practitioners use explainability in industry. Through an interview study, we find that, while engineers …
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Paths to AI Accountability: Design, Measurement, and the Law
… years, the falling barrier between humans and AI has sparked fears about AI’s capabilities and elicited questions about the role that algorithms and, increasingly, AI should play in our lives. As society continues working towards answering these questions, this thesis argues that we must …
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