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 20 for “"Algorithmic Fairness"”.
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Algorithmic Fairness in Sequential Decision Making
… solutions have been proposed for achieving fairness in one-shot decision-making, there is a gap in investigating the long-term effects of sequential algorithmic decisions. In this thesis, we focus on studying algorithmic fairness in a sequential decision-making setting. We first study how to …
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Clinical Presence: Impact on Predictive Modelling and Algorithmic Fairness
… presence on predictive modelling and associated algorithmic fairness properties. We propose to use machine learning to tackle scalability and modelling flexibility while accounting for statistical biases and potential socio-medical disparities associated with clinical presence. By connecting …
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TOWARDS HUMAN-CENTRIC AI: INVERSE REINFORCEMENT LEARNING MEETS ALGORITHMIC FAIRNESS
… of human-centric AI - value alignment and fairness. Our first work explores Inverse reinforcement learning (IRL), a potential solution to value alignment. We introduce BO-IRL, an IRL algorithm that uses a novel kernel to explore the reward function space efficiently. The second work …
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Evaluating and enhancing cardiovascular disease risk prediction with algorithmic fairness
… in predictions can exacerbate health inequities. Algorithmic fairness, a research area in machine learning, provides a quantitative approach to assess and address inequities in prediction models. This thesis uses the principles of algorithmic fairness to evaluate CVD risk prediction models using …
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REFRAMING ALGORITHMIC FAIRNESS: A PARADIGM FOR FAIR, ACCURATE, AND FLEXIBLE MODEL DEVELOPMENT
… recourse or repair. This dissertation develops algorithmic tools for responsible and flexible machine learning, focusing on models that can adapt post-deployment, incorporate human feedback, and scale to real-world use. It introduces bias bounties--- a framework for users to collaboratively …
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Context-conscious fairness throughout the machine learning lifecycle
… a proliferation of literature seeking to define “fairness” narrowly as an error to be “fixed” and to quantify it as an algorithm’s deviation from a formalised metric of equality. Dozens of notions of fairness have been proposed, many of which are both mathematically incompatible and morally …
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Online Communities and Health
… help others. The second study investigates how algorithmic fairness, accountability, and transparency of an OSN newsfeed algorithm influence the users' attitudes and beliefs about childhood vaccines and ultimately their vaccine hesitancy. The third study examines how OSN social overload, through …
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Imaginative reasoning in probabilistic programs
… allows us for instance to address problems of algorithmic fairness, robustness, and perform parameter estimation using data about probabilities, expectations and other distributional properties. The second is causal inference, which allows us in complex simulation models to reason about …
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Software packages performance evaluation of basic radar signal processing techniques
… was decided that the implementations should have algorithmic fairness across the various software packages. The first experiment was loop based pulse compression and Doppler processing algorithms, where Julia and Python outperformed the rest. A further analysis was completed by using vectors to …
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Just(ifying) algorithms: Data-driven automated predictions about unobservable targets and the General Data Protection Regulation
… the GDPR. It first extends the discourse on algorithmic fairness by delineating inherent technical limitations of data-driven prediction. Distinguishing between targets of interest which are unobservable, evaluative and merely unobserved, the thesis identifies a number of irreducibly …
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Methods to Improve Fairness and Accuracy in Machine Learning, with Applications to Financial Algorithms
… to reconcile two influential criteria for algorithmic fairness that were previously thought to be in conflict: calibration and equal error rates. We present an algorithm that identifies the most accurate set of predictions satisfying both conditions. In a credit-lending application, we …
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Data-centric Approaches for Responsible Data Science
… revolves around responsible data science and algorithmic fairness with a strong emphasis on data-centric approaches. In this study, we firstly focus on data coverage as a data-centric approach for identifying and resolving the misrepresentation of minorities in data. We propose novel …
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A post-processing framework for group fairness
… mislabeled Black defendants as high risk. Group fairness definitions, including statistical parity and equal opportunity, formalize such disparities and form the basis of many fair learning algorithms. However, the effectiveness of these algorithms is often hindered by practical challenges. When …
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Algorithmic foundation of fair graph mining
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms
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The Objective Function: Science and Society in the Age of Machine Intelligence
… they work with, and the artifacts they produce—algorithmic systems, public demonstrations of machine intelligence, academic research articles, and conference presentations—a wider set of implications about the legacies of positivism and objectivity, the construction of expertise, and the …
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Evaluating, Understanding, and Mitigating Unfairness in Recommender Systems
… such as education and employment. Biases and unfairness in recommendations raise both ethical and legal concerns. In this dissertation, we investigate the concept of unfairness in the context of recommender systems. In particular, we study appropriate unfairness evaluation metrics, examine the …
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Out-of-distribution generalisation in machine learning
… fill an existing gap between data-centric and algorithmic approaches to out-of-distribution generalisation. These theoretical findings guide a new set of practical recommendations on how to employ the algorithmic approach. In the second contribution, I tackle generalisation in the common …
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Achieving Differential Privacy and Fairness in Machine Learning
… learning algorithms on matters like privacy and fairness. Currently, many studies only focus on protecting individual privacy or ensuring fairness of algorithms separately without taking consideration of their connection. However, there are new challenges arising in privacy preserving and …
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Abstraction and application: complementary perspectives on sociotechnical systems
Algorithmic and technological systems are often developed using abstract models and objectives, but are ultimately used for specialized real-world applications; the gap between these levels of design and use often leads to unmet user needs or amplified harms at scale. Addressing this gap benefits …