Manchester, NA, United Kingdom Funded PhD Project Full time
This PhD will aim to optimise the use of AI and machine learning for the implementation of ‘covert situational integrity testing’ as a mechanism for assessing corporate and organisational compliance with legal rules and standards, the goal being to enhance corporate accountability. There are major challenges to holding corporations/organisations to account for non-compliance: low detection levels, incomplete understanding of the inner workings of organisations, and the modest power of current social scientific research methodologies. This PhD will explore the potential for methodological innovation through the use of automated, online, covert situational integrity testing, a variation of mystery shopping methodologies, to address some of these gaps, with a focus not on service quality or customer satisfaction but on compliance with regulatory and legal requirements, and as a data gathering tool on secretive and difficult to access areas of business operation.
The applicant will need knowledge and understanding of AI, machine learning and deep learning, and a social scientific understanding of the topic area and social research methods. The project will focus on assessing anti-money laundering compliance by financial institutions and unlawful tax minimisation. The project aims to generate an AI-based tool to implement covert situational integrity testing in order to provide a research mechanism through which robust and systematic observational data can be collected and scrutinised by independent, third-party assessors to understand levels of organisational/corporate compliance with legal rules and standards, and by doing so, identify critical vulnerabilities and strengths in the compliance responses of organisations and industries.
Academic Requirements:
* Bachelor's (Honours) degree in a cognate subject at 2:1 or above (or overseas equivalent); and
* Master's degree in a relevant subject - with an overall average of 65% or above, a minimum mark of 65% in your dissertation and no mark below 55% (or overseas equivalent)
All applicants must provide evidence of English language proficiency.
If you have any questions or would like to discuss this further, please contact Prof. Nicholas Lord (nicholas.lord@manchester.ac.uk).
Formal interviews are expected to take place week commencing TBC.
The project is supported by the University of Manchester's AI Trust and Security initiative. The studentship provides funding for 4 years, beginning in September 2025. It covers tuition fees and an annual UKRI stipend (2024/25 rate £19,237 per annum).
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