PhD Studentship: Extending the explainability of machine learning models in policy decision making

Project summary: Governments and policy makers are increasing their use of machine learning (ML) to support decision-making. The performance of ML algorithms generally improves with the increase of model complexity, which makes it harder for end-users to interrogate the model output. This PhD project aims to bridge the gap by developing a novel process and framework to ensure that ML models can be better understood, and therefore more readily adopted by policy makers.

Deadline: 13 April 2023

Duration: 36 months full-time

Funding details: Fully-funded scholarship for 3 years covers all university tuition fees (at UK level) and an annual tax-free stipend. International students are also eligible to apply, but they will need to find other funding sources to cover the difference between the home and international tuition fees. Exceptional international candidates may be provided funding for this difference.

Number of places: 1

Eligibility: 1st class first degree and/or an excellent Masters-level qualification or overseas equivalent in a relevant computer science, management science, operations research, mathematics and statistics, and data science from a recognised academic institution.

Project details: This PhD project aims to develop a novel processes and frameworks to ensure that machine learning algorithms can be explained and therefore more readily adopted by policy makers.

Specifically, the project aims to answer the following research questions:

1. Does integrating ML models with decision support tools provide a framework to ensure increased adoption of ML models by policy makers?
2. How can uncertainty and modelling choices inherent in the machine learning algorithms be visually represented to ensure that policy makers better understand the ML process and output?
3. How useful is this new approach to improve policy makers’ confidence in using ML models and inform industry-wide policy changes?

Primary supervisor: Dr Bin Liu

Contact: b.liu@strath.ac.uk

Full details   

Advert information