Causality and Counterfactuals in Explainable AI

These scholarships will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance) and will provide a £900 per annum research training support grant (RTSG) to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Please note: you will automatically be entered into the competition for the Full Award, unless you state otherwise in your application.

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,000 (tbc) per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

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[2] J. Pearl, The seven tools of causal inference, with reflections on machine learning, Commun. ACM 62 (2019), 54-60.

[3] A. Holzinger, G. Langs, H. Denk, K. Zatloukal, H. Müller, Causability and explainability of artificial intelligence in medicine, Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 9 (2019), e1312.

[4] Y-L. Chou, C. Moreira, P. Bruza, C. Ouyang and J. Jorge, Counterfactuals and causability in explainable artificial intelligence: Theory, algorithms, and application’, Information Fusion 81 (2022), 59-83.

[5] D.H. Glass, Competing hypotheses and abductive inference, Annals of Mathematics and Artificial Intelligence, 89 (2019), 161-178.

[6] D.H. Glass, How good is an explanation?, Synthese, 201 (2023), 53.

[7] J.G. Richens, C.M. Lee, and S. Johri, Improving the accuracy of medical diagnosis with causal machine learning, Nature Communications, 11 (2020), 3923.

[8] C. Rudin, Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence 1 (2019), 206–215.

[9] N. Pawlowski, D. Coelho de Castro, and B. Glocker, Deep structural causal models for tractable counterfactual inference, Advances in Neural Information Processing Systems, 33 (2020), 857-869.