Artificial Intelligence Empowered Human-Machine Partnership for EXtended Reality ensuring Safety and Human-centric Solutions

The first ever AI Safety Summit, held in the UK in November 2023, has highlighted many challenges associated with the uncertainties surrounding the future of Artificial Intelligence (AI) and the rapid pace at which this dynamic field is evolving. 28 countries signed the Bletchley Declaration, an agreement to commit to designing AI that is safe and human-centric.

The advancement of AI-led rapid transformations urgently requires a robust bridge between humans and machines that enable more sophisticated collaborative partnerships, rather than the traditional "ownership" model to address and reduce public concern and enable innovation that can deliver common good for humanity and the environment. This AI-empowered Human-Machine Partnership (HMP) will help ensure fairness, transparency, explainability, governance and redress without stifling innovation and transformative AI.

This project will deliver a next generation transformative AI-empowered HMP solution for an Extended Reality (XR) area within our state-of-the-art Spatial Computing and Neurotechnology Innovation Hub (SCANi-hub). The SCANi-hub and new proposed additions for XR Health Hub (subject to business case) leverage cutting edge technology to analyse the body and brain’s responses to various stimuli, including stress, fatigue, achievement, and awareness in complex training and performance assessment scenarios simulated within virtual environments.

This PhD will explore the following themes:

• Human-Machine Partnerships / Human AI Collaboration
• Responsible and safe AI
• Ethical AI
• AI regulation
• Explainable AI
• AI in healthcare

Barriers such as algorithmic bias, data access limitations, regulatory challenges, replacement of workforce are all involved in hesitancy for adoption and the major challenge of human factors such as trust in AI will be investigated:

• Why there is a lack of trust in AI for healthcare?
• Is this warranted?
• How can trust be improved?
• Trust outcomes of clinicians and patients before and after being asked to pilot AI tools for healthcare

Please note that a research proposal is NOT required for this project.

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 comprehensive and articulate personal statement
• A demonstrable interest in the research area associated with the studentship

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

The University offers the following levels of support:

The following scholarship options are available to applicants worldwide:

• Full Award: (full-time tuition fees + £19,000 (tbc))
• Part Award: (full-time tuition fees + £9,500)
• Fees Only Award: (full-time tuition fees)

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.

• Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
• Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
• Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
• 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.

Due consideration should be given to financing your studies. Further information on cost of living

[1] http://www.midasproject.eu/

[2]http://magic-pcp.eu/

[3] https://hope.be/EU_Projects/lucia/

[4] Rjoob K, McGilligan V, McAllister R, Bond R, Doolub G, Leslie SJ, Manktelow M, Knoery C, Shand J, Iftikhar A, McShane A, Mamas MA, Peace A; EAPCI Innovation and Digital Cardiology Committee. What do we mean by complex percutaneous coronary intervention? An assessment of agreement amongst interventional cardiologists for defining complexity. Catheter Cardiovasc Interv. 2023 Jul;102(1):1-10. doi: 10.1002/ccd.30684. Epub 2023 May 20. PMID: 37210623.

[5] Bond RR, Novotny T, Andrsova I, Koc L, Sisakova M, Finlay D, Guldenring D, McLaughlin J, Peace A, McGilligan V, Leslie SJ, Wang H, Malik M. Automation bias in medicine: The influence of automated diagnoses on interpreter accuracy and uncertainty when reading electrocardiograms. J Electrocardiol. 2018 Nov-Dec;51(6S):S6-S11. doi: 10.1016/j.jelectrocard.2018.08.007. Epub 2018 Aug 10. PMID: 30122457

[6] Peace A, Al-Zaiti SS, Finlay D, McGilligan V, Bond R. Exploring decision making 'noise' when interpreting the electrocardiogram in the context of cardiac cath lab activation. J Electrocardiol. 2022 Jul-Aug;73:157-161. doi: 10.1016/j.jelectrocard.2022.07.002. Epub 2022 Jul 10. PMID: 35853754