Research Fellow

Location: Stirling Campus

The Post

Participatory Harm Auditing Workbenches and Methodologies (PHAWM) is a £4M project funded by Responsible AI UK, led by Dr Simone Stumpf of the University of Glasgow, and spanning six universities with partners including Fujitsu, Meta, Microsoft, NHS and the Scottish Government. PHAWM will investigate the systematic and participatory auditing of current and evolving AI technologies in terms of accuracy, bias and fairness as well as trade-offs between them by stakeholders with diverse levels of expertise. Our research will create auditing workbenches comprising novel user interfaces, algorithms and privacy-preserving mechanism that underpin participatory approaches accessible to domain experts, regulators, affected parties, and the public. We will advance systematic auditing methodologies embedded in auditing workbenches which reflect, anticipate and inform regulatory frameworks.

This post is part of the University of Stirling’s contribution to PHAWM, led by Dr Sandy Brownlee and Dr Leonardo Bezerra, which will develop supporting technologies for PHAWM. This will focus on building multi-objective optimisation tools to explore trade-offs between fairness and accuracy, and integrating the novel fairness measures and model interventions developed in the project.

To inform development of these tools, the postholder will also engage with the initial stakeholder research to ensure the tools are suitable for end-users. The postholder will also support evaluations of the proposed methodology, working to refine the tools as needed in the later stages of the project, and support development of a certification framework, informed by their work on the tool development.

10% of the post’s time will provide the postholder with opportunities to build their track record through work in other applied research projects and supporting teaching at Stirling.

Description of Duties

• Assist the delivery of PHAWM and other research projects by, for example, implementing algorithms, collating data and undertaking and recording the outcomes of experiments, and working with stakeholders to develop models
• Write up results of own research and contribute to the production of research reports and publications
• Engage in impact and dissemination activities such as conferences, blogs, and webinars and other knowledge exchange activities to encourage societal impact
• Assist with the supervision of undergraduate and postgraduate student projects and the delivery of teaching assistance as required
• Manage and prioritise own workload within agreed objectives to ensure that all activities are completed within set deadlines

Essential Criteria

• PhD in optimisation and learning
• Possess sufficient breadth or depth of specialist knowledge in machine learning and optimisation and of relevant research methods and techniques to work within established research programmes
• Excellent knowledge in implementing machine learning tools in software and relevant research methods
• Excellent oral and written communication skills including public presentations and ability to communicate complex data/concepts clearly and concisely

For further information, including a full description of duties, essential criteria and details on how to apply, please see Vacancy details | University of Stirling

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