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

Project Introduction:

This proposed research aligns to The new Advanced Research and Engineering centre 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.

This research project aligns to the workflow and AI streams within the Centre. A selection process will determine the strongest candidates across a range of projects, who may then be offered funding for their chosen project. Approximately £6000 per year is payable to the sponsored student 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.

Project Key Words:

Artificial Intelligence, Neural Networks, Digital Work, Business Service,. Ethics in A.I. Application

Full-Time    

Start Date: 01 / 10 / 2022

Application Closing date: 28 / 02 / 2022

Funding Body: DfE/External Corporate Funding.

Project Funding Type: Funded

Funding Information:

This three year studentship, for full-time PhD study, is potentially funded by the Department for the Economy (DfE) and commences on 1 October 2022. For UK domiciled students the value of an award includes the cost of approved tuition fees as well as maintenance support (Fees £4,500 pa and Stipend rate £15,609 pa - 2022-23 rates to be confirmed). To be considered eligible for a full DfE studentship award you must have been ordinarily resident in the United Kingdom for the full three year period before the first day of the first academic year of the course.  The candidate must be ordinarily resident in Northern Ireland on the first day of the first academic year of the course, normally 1 October. For further information about eligibility criteria please refer to the DfE Postgraduate Studentship Terms and Conditions 2021-22 at https://go.qub.ac.uk/dfeterms

A selection process will determine the strongest candidates across a range of projects, who may then be offered funding for their chosen project. This is an industrially sponsored project. Approximately £6000 per year is payable to the sponsored student in addition to the stipend rate detailed above. Bringing the total stipend to approximately £21,609 per annum.

For candidates who do not meet the above residency requirements, a small number of international studentships may be available from the School. These are expected to be highly competitive, and a selection process will determine the strongest candidates across a range of School projects, who may then be offered funding for their chosen project. 

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.

Applicants should apply electronically through the Queen’s online application portal at: https://dap.qub.ac.uk/portal/