Developing Novel Approaches for Explainable AI: Enhancing Transparency and Trust in Black-Box Models

Summary

This PhD aims to address a critical challenge in artificial intelligence: enhancing the transparency and trustworthiness of "black-box" models through Explainable AI (XAI). As AI systems increasingly impact fields like healthcare and finance, their opaque nature presents significant ethical, legal, and operational concerns. The core issue lies in the trade-off between model accuracy and interpretability. While state-of-the-art AI models, such as deep neural networks, reinforcement learning and generative AI are highly accurate, they often lack transparency. In contrast, simpler models are more interpretable but less capable in handling complex tasks.

This research proposes to bridge this gap by developing novel XAI techniques that maintain model performance while improving interpretability. Moving beyond current methods such as post-hoc explanations (LIME, SHAP, Grad-CAM), this study seeks to establish robust, user-centric approaches that meet the needs of diverse stakeholders, including data scientists, policymakers, and end users.

Key objectives include designing model-agnostic explainability techniques for complex models, real-time interpretability for dynamic applications like healthcare, and customised explanations for different audiences. The project will also integrate fairness and bias detection in high-stakes areas, including healthcare, finance, and law.

The research methodology will combine theoretical framework development, algorithm design, and empirical testing. Theoretical work will draw on information theory, causality, and cognitive science to formalise interpretability. New algorithms will enhance transparency, scalability, and accessibility for technical and non-technical users. Empirical validation using domain-specific datasets will rigorously evaluate interpretability, accuracy, and fairness.

This research will contribute to the field of XAI by developing explanation methods that are robust, fair, and suited to real-time, high-stakes applications. Expected contributions include new interpretability tools, methods for bias detection, and user-friendly solutions to enhance public trust in AI. The proposal aligns with industry and societal demands for ethical AI and promises significant academic and practical advancements.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• Experience using research methods or other approaches relevant to the subject domain
• A demonstrable interest in the research area associated with the studentship

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• First Class Honours (1st) Degree
• Masters at 70%
• For VCRS Awards, Masters at 75%
• Experience using research methods or other approaches relevant to the subject domain
• Work experience relevant to the proposed project
• Publications - peer-reviewed
• Experience of presentation of research findings

Equal Opportunities

The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.

Appointment will be made on merit.

Funding and eligibility

This project is funded by:

• Department for the Economy (DfE)