Information on this PhD research area can be found further down this page under the details about the Widening Participation Scholarship given immediately below.
Applications for this PhD research are welcomed from anyone worldwide but there is an opportunity for UK candidates (or eligible for UK fees) to apply for a widening participation scholarship.
Widening Participation Scholarship: Any UK candidates (or eligible for UK fees) is invited to apply. Our scholarships seek to increase participation from groups currently under-represented within research. A priority will be given to students that meet the widening participation criteria and to graduates of the University of Salford. For more information about widening participation, follow this link: https://www.salford.ac.uk/postgraduate-research/fees. [Scroll down the page until you reach the heading “PhD widening participation scholarships”.] Please note: we accept applications all year but the deadline for applying for the widening participation scholarships in 2024 is 28th March 2024. All candidates who wish to apply for the MPhil or PhD widening participation scholarship will first need to apply for and be accepted onto a research degree programme. As long as you have submitted your completed application for September/October 2024 intake by 28 February 2024 and you qualify for UK fees, you will be sent a very short scholarship application. This form must be returned by 28 March 2024. Applications received after this date must either wait until the next round or opt for the self-funded PhD route.
Project description: The proposed PhD project aims to investigate and address the issue of fairness in machine learning systems. As machine learning algorithms are increasingly being used in various domains, from healthcare and criminal justice to employment and education, concerns about bias in these systems have gained prominence. This research will focus on understanding the different types of biases that can exist in machine learning systems, including algorithmic bias, data bias, and decision-making bias.
The project will involve a thorough analysis of existing machine learning models and datasets to identify potential sources of bias. Advanced techniques, such as reweighing, adversarial training, and fairness-aware machine learning, will be utilised to mitigate bias and promote fairness in machine learning systems. The research will also explore ethical considerations and implications associated with fairness in machine learning, including the trade-offs between fairness, accuracy, and interpretability.
The expected outcome of this project will be a comprehensive understanding of AI fairness in machine learning systems, including the development of novel techniques for bias mitigation and recommendations for practitioners and policymakers. The research findings will be disseminated through publications in reputable conferences and journals, and will contribute to the growing body of knowledge on AI fairness, with the aim of promoting transparency, accountability, and fairness in machine learning systems.