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 9 of 9 for “"Fair machine learning"”.
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Beyond traditional assumptions in fair machine learning
… common assumptions underlying traditional machine learning approaches to fairness in consequential decision making. After challenging the validity of these assumptions in real-world applications, we propose ways to move forward when they are violated. First, we show that group fairness …
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Robust and Fair Machine Learning under Distribution Shift
<p>Machine learning algorithms have been widely used in real world applications. The development of these techniques has brought huge benefits for many AI-related tasks, such as natural language processing, image classification, video analysis, and so forth. In traditional machine learning …
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Measuring justice in machine learning
How can we build more just machine learning systems? To answer this question, we need to know both what justice is and how to tell whether one system is more or less just than another. That is, we need both a definition and a measure of justice. Theories of distributive justice hold that justice …
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Fairness and Privacy in Machine Learning Algorithms
… nearly impossible but with the widespread use of machine learning algorithms and their ability to process enormous data in a fast, cost-effective, and scalable way has proven to be a preferred choice to glean useful insights and solve business problems in many domains. With this widespread use of …
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Advancing Precision in Prenatal Depression Prediction: Leveraging EMRs and Enhancing ML-Model Fairness
Machine learning models for predicting perinatal depression (PND) from electronic medical records (EMRs) show promise for early intervention, yet significant performance disparities (model bias) across sociodemographic groups limit their clinical utility for diverse populations. Current EMR-based …
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Machine Learning in Consumer Credit: Legal, Economic, Ethical & Policy Implications
… the integration of artificial intelligence and machine learning offers potential solutions to these persistent challenges, but also risks exacerbating existing problems. This research challenges pervasive binary assumptions that the introduction of artificial intelligence will either inherently …
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Identifying, Measuring, and Addressing Algorithmic Bias in AI Admission Systems for Graduate Education
… data and algorithmic bias, which can lead to unfair outcomes for underprivileged subgroups among applicants. Recent changes in legislation such as the ban of affirmative action by the U.S. Supreme Court make it increasingly relevant to study the demographic composition of admitted students and …
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AI-in-the-loop human interventions for homelessness resource allocation
… towards developing a demographic parity group fairness criterion based intervention to improve the equity in outcomes associated with the Austin Prioritization Index Coordinated Assessment. Our findings result in the proposal of an AI-in-the-loop assistive decision system to augment and improve …
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Optimization in Deep Learning: Structured, Realistic and Interpretable Learning for Decision-Making
In recent years, deep learning has emerged as a powerful tool for data-driven decisionmaking. However, its adoption in high-stakes applications is often constrained by challenges related to interpretability, fairness, and generalization in structured or complex environments. This thesis develops …