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Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project.

The PhD will be based in the School of Organisations, Systems and People and will be supervised by Professor Mark Xu, Dr Salem Chakhar and Dr Muhammad Shakir.

Emerging technologies, such as artificial intelligence (AI), machine learning (ML), drive many businesses changes through digital transformation, managers are faced with increased uncertainty and dynamic environment in decision making, usually measured by complexity of the environment variables and the rate of change of these variables. The challenges can be viewed from two perspectives. First, on managerial decision making, there are an increasing number of triggers, speculative information, fake news, misinformation and disinformation (Petratos, 2021) that senior managers need to attend to. Second, on organisational decision making, it is well documented that routine tasks and decisions can be handled efficiently by automation and AI, as the variables for these tasks and decisions tend to be frequent, repetitive, relatively structured, hence rules and programs can apply. Whereas nonroutine tasks and decisions in high uncertain situations pose great challenges for AI because a) the situation is unfamiliar, unique, infrequent and heterogeneous, hence variables cannot be easily defined; b) relevant data and knowledge may not be readily available and accessible; c) algorithms may not incorporate all assumptions and models; d) managers may not trust the answers given by the AI due to various reasons - lack of transparency, incorrect decisions, high risks, unethical, and unclear responsibilities (Barredo Arrieta et al., 2020; Mikalef,et al. 2022; Vaia, et al. 2022 ).  As a result, there is growing research suggesting that AI and algorithms should be used in caution as this may lead to algorithmic/automation bias (Ferguson, 2017, Rai et al 2019, Sen et al 2020).

This research aims to advance how AI can be better developed and deployed for managerial decisions in uncertainty. The candidate should select one of the two topics listed below: 

Topic 1: To develop a framework of responsible AI for management decision with consideration of organisational and technical entities including digital governance, risk management and ethics, etc. test and validate the framework using appropriate approaches.

Topic 2. To design and develop an AI decision tool to support management decisions in uncertain situations with consideration of embedding responsible AI elements, adopting emerging Open AI algorisms, test and validate the tool in a real or simulated decision situation.

The focus and scope of both - topics is closely related to each other, however, the first topic will lead to the development of a conceptual framework of a responsible AI informed by emerging AI technologies, new organisational settings and governing protocols,  while the second topic will focus on the design and validation of - a responsible AI based decision tool for complex managerial decisions.

References 

A. Rai and P. Constantinides and S. Sarker (2019). Next Generation Digital Platforms: Toward Human-AI Hybrids. MIS Quarterly, 43(1), iii--ix.

A.G. Ferguson (2017). The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement .NYU Press. doi: 10.2307/j.ctt1pwtb27}

Mikalef, P., Conboy, K., Lundström, J. E., & Popovič, A. (2022). Thinking responsibly about responsible AI and ‘the dark side’ of AI, European Journal of Information Systems, 31(3), 257-268.

Petratos, P. N. (2021). Misinformation, disinformation, and fake news: Cyber risks to business. Business Horizon, 64(6), 763-744.   

S. Sen,  D. Dasgupta, K.D. Gupta (2020). An Empirical Study on Algorithmic Bias. In: IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), pp. {1189-1194. doi: 10.1109/COMPSAC48688.2020.00-95

Vaia, G., Arkhipova, D. & DeLone, W. (2022). Digital governance mechanisms and principles that enable agile responses in dynamic competitive environments. European Journal of Information Systems, 31(6), 662-680.

Fees and funding

Visit the research subject area page for fees and funding information for this project.

Funding availability: Self-funded PhD students only. 

PhD full-time and part-time courses are eligible for the UK  (UK and EU students only – eligibility criteria apply).

Bench fees

Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.

We’d encourage you to contact Professor Mark Xu (mark.xu@port.ac.uk) to discuss your interest before you apply, quoting the project code.

Please also include a research proposal of 1,000 words outlining the main features of your proposed research design – including how it meets the stated objectives, the challenges this project may present, and how the work will build on or challenge existing research in the above field. 

When applying please quote project code:OS&P4921024