Symbiotic human-AI decision support system to improve group decision-making under uncertainty

In daily life, individuals often make decisions that are influenced by incomplete information or the decisions of other individuals. Decision in the presence of incomplete information are frequently characterized by low confidence and errors, and group decision-making tends to outperform individual decisions, capitalizing on the 'wisdom of crowds'. Recent advancements explore the integration of Brain-Computer Interface (BCI) technology as a support system to improve group decision-making. Such systems combine inputs from multiple users to improve the overall decision quality. However, these systems are susceptible to performance degradation due to factors such as mental fatigue and disengagement.

This research project builds upon our team's expertise in computational modelling for decision-making and BCI-based decision support systems by developing a symbiotic decision support system that merges human judgement and autonomous AI to improve group decision-making, particularly in time-sensitive, complex scenarios. The autonomous AI will act as an equitable partner to human team members while simultaneously monitoring human performance.

This project will serve as a catalyst for future research in Human-Machine Teams and Neurotechnology to boost productivity in both public and commercial sectors.

The PhD candidate will benefit from the research centre’s expertise in Computational Neuroscience, Neurotechnology and AI, and state-of-the-art facilities in neuroimaging and high-performance computing, paving the way for promising and exciting opportunities in a career in AI, big data analytics and computational social science. The candidate will also interact with leading national and international collaborators. In 2021, Ulster University was ranked 2nd in the UK for Ph.D. researcher satisfaction, 6th largest Computer Science and Informatics unit, and 7th for the level of world-leading or internationally excellent research and impact with respect to staff number.

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. D. Ban and C.D. Frith. (2017) Making better decisions in groups. Royal Society Open Science, 4170193170193. http://doi.org/10.1098/rsos.170193

2.   S. Bhattacharyya, D. Valeriani, C. Cinel, L. Citi and R. Poli. (2021) Anytime collaborative brain–computer interfaces for enhancing perceptual group decision-making. Scientific Reports 11, 17008. https://doi.org/10.1038/s41598-021-96434-0

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4. K.C. Ewing and C. Borras. (2022) Quantified minds: Predicting human functional state for human-machine teaming. Ergonomics & Human Factors Conference.

5. R.G. O’Connell, M.N. Shadlen, K. Wong-Lin and S.P. Kelly (2018) Bridging neural and computational viewpoints on perceptual decision-making. Trends in Neurosciences, 41(11):838-852. DOI:https://doi.org/10.1016/j.tins.2018.06.005

6. N.A. Atiya, I. Rañó, G. Prasad and K. Wong-Lin (2019). A neural circuit model of decision uncertainty and change-of-mind. Nature Communications 10 (1), 2287. https://doi.org/10.1038/s41467-019-10316-8