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A.I. driven Digital Workers (DW) for Business Services

   School of Electronics, Electrical Engineering and Computer Science

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  Dr KR Rafferty, Dr Barry Devereux  No more applications being accepted  Funded PhD Project (European/UK Students Only)

About the Project

Project Introduction:

This proposed research aligns to the new Advanced Research and Engineering centre (ARC) within Northern Ireland. This Centre will drive future innovations in technology and enhance our capabilities in important research areas such as robotic process automation (RPA), workflow automation, visualisation, data analytics and artificial intelligence (AI). The Centre brings together expertise from PwC, University of Ulster and Queen’s University Belfast. PwC are a collaborating partner on this project and have committed to providing co-location, sharing of data and co-supervision, along with an additional cash contribution of approximately £6000 per year in addition to the normal stipend. 

A digital worker is an automated piece of software designed and developed to perform parts of some traditionally defined job role. After years of development, machine automation has reached a point that enables digital workers to free humans from some repetitive and labour intensive work. This allows the human to focus more on the value-added part, and this has been shown to increase productivity and user satisfaction. More recently, deep learning applied to digital working has been shown to achieve human level performance and even outperform humans in a number of popular and simple tasks.

This state of the art research motivates the development and deployment of digital workers based on deep neural networks (DNNs) within the business supply chain of some professional services.

Replacing human work with robotic/automata work raises significant ethical issues and these will studied as part of the project.

Project Description:

The main specific aim of this project is to deliver a transparent, fair, understandable and accountable digital worker driven by deep learning. 

DNNs are increasingly used in place of traditionally engineered software in many areas. This project will explore the most promising approaches for the development of a digital worker to perform professional business support services that have a high degree of reliability. However, DNNs are complex non-linear functions with algorithmically generated (and not engineered) coefficients, and therefore are effectively “black boxes”. Hence, in this project, we are not simply developing DNNs for digital worker tasks but also carrying out quantitative evaluation against stringent metrics to evaluate the performance of specific tasks (to be identified as part of the project.)

The project will also seek to identify a rationale for why a digital worker DNN performs in a particular manner so that stakeholders may understand and appropriately trust the work done by a digital worker.

Once the broad stroke of the capabilities of a DNN based DW have been proved (against statistical benchmarks) the ethics of deploying such a technology will be explored so as to provide evidence for or against its use, or when or when not to use it, for example in providing a backup service when the equivalent human service may be unavailable.

The key technological objective is:

  • To develop a DNN for performing a defined digital worker task using benchmark case studies.

The key ethical objectives are:

  • To generate evidence for explaining the digital worker DNN and any result from the worker.
  • To examine the effects of deployment of DW are full or partial replacements for human workers.


Start Date:  01 October 2023

Application Closing date: 28 February 2023

Academic Requirements:

A minimum 2.1 honours degree or equivalent in Computer Science, Electrical and Electronic Engineering, or Psychology or relevant degree with relevant technological experience.

To Apply please complete an application through the Direct Applications Portal:

Funding Notes

Funding Body: DfE CAST, PwC
To be eligible for consideration for a DfE Studentship (covering tuition fees and maintenance stipend of approx. £17,668 per annum), a candidate must satisfy all the eligibility criteria based on nationality, residency and academic qualifications. The Studentship is open to UK and ROI nationals, and to EU nationals with settled status in the UK, subject to meeting the specific DfE nationality and residency criteria. Full eligibility information can be viewed via:
This is an industrially sponsored project. Approximately £6000 per year is payable to the sponsored student in addition to the stipend rate detailed above.
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