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Talent analytics, algorithmic decision making and access to work and career opportunities

   Leeds University Business School

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  Prof Andy Charlwood, Dr Danat Valizade  No more applications being accepted  Competition Funded PhD Project (UK Students Only)

About the Project

This ESRC WRDTP Associated Studentship is located in the ESRC Digital Futures at Work Research centre (Digit). The research will focus on use of machine learning and AI-enabled decision making in recruitment, selection and promotion in order to better understand how the digitisation of this critical area of HR operations is changing access to work, affects career progression and existing inequalities at work.

The Studentship will thus support a programme of research that aims to understand how machine learning, algorithmic management and AI are being used in the recruitment and selection process and what the implications of this use is for access to work and careers for different groups in society. Drawing on research from the sociology of work and organisations, human resource management and information systems, it will examine the developing market for algorithmic recruitment and talent management tools, the organisations that are purchasing tools and how they are using them and the implications this has for job applicants and their career prospects.

A key aim of this ESRC WRDTP Associated Studentship is to provide the successful applicant with high quality research training and professional development opportunities. The Studentship will be based in the Digital Futures at Work Research Centre (Digit) at the University of Leeds. Digit was launched in January 2020, funded by a £6.5 million award from the ESRC, and is co-directed from the University of Leeds (Professor Mark Stuart) and the University of Sussex (Professor Jackie O'Reilly), in partnership with the Universities of Aberdeen, Cambridge, Manchester and Monash. Digit offers an outstanding support structure for PGR research, with an extensive network of established academics in the UK and internationally and a well-developed programme of early career training and mentoring. The successful applicant will benefit from the support structures and wider research activities of Digit, as well as the outstanding programme of training offered by the University of Leeds. They will also receive close support, mentoring and guidance from the academic supervisors, Professor Andy Charlwood and Dr Danat Valizade.

The project will sit within the core research programme of Digit, specifically Research Themes and 3, which examine employers’ digital practices at work and employees’ experiences of new forms of digital management practice.

A recent report by the UK Government’s Centre for Data Ethics and Innovation (CDEI) found evidence of rapid growth in the use of algorithmic tools at all stages of the recruitment and selection process. While use of algorithms in recruitment has the scope to dramatically improve efficiency and reduce the incidence of well-known biases in human decision-making, there are also significant risks that algorithmic decision-making will further disadvantage women and minority groups. The CDEI found that organisations using algorithms in recruitment decision-making often lacked an understanding of how to use algorithms in a way that does not entrench existing inequalities. Further, current best practice guidance on use of algorithms in recruitment is often not followed and new regulation may be needed to safeguard against unfair and discriminatory practice.

It is therefore important to develop empirical evidence and theoretically informed analysis of why, how and with what consequences algorithms are being used in recruitment and talent management. The exact methods used can be adapted to the preferences and skills of the successful applicant, but are likely to include some mix of case-studies of specific algorithmic recruitment and/or talent management tools; how did they come into being (design and funding)? Who buys them for what purpose? How are the tools used in practice (perhaps employing ethnographic methods)? What impact do these tools have (through secondary analysis of the Digit employer survey, datasets provided by companies or field experiments) on the access to employment of minority and disadvantaged groups?

The expectation is that the student will have a good social science background, ideally with an understanding of contemporary HRM, organisational sociology and/or the sociology of work and employment.


Two types of studentship are available depending on the level of social science research training already completed.

1+3 Studentships: an integrated Research Training Masters – MA Social Research (Interdisciplinary – delivering the majority of the core training requirements) followed by a 3-year PhD programme.

+3 Studentships: 3-year PhD (applicants must demonstrate that they have already completed substantial social sciences training in research methods which would enable them to undertake an independent research project in a particular discipline or interdisciplinary field. An applicant must have at least 60 credits at Masters level of core social sciences research methods acquired in the last five years. This must include a broad range of methods, including quantitative, qualitative and mixed methods and the use of appropriate software/tools for their application, and comprehension of principles of research design and strategy, and an appreciation of alternative approaches to research).

Information about the Award

  • This competition is open to UK applicants only. One studentship will be awarded.


  • The award will cover full fees at the University of Leeds standard UK rate of fees.
  • A maintenance grant at the standard UKRI rate (£15,609 in Session 2021/22).

Other Conditions

  • Applicants must not have already been awarded or be currently studying for a doctoral degree.
  • 1+3 awards must be taken up in September 2021 and +3 awards must be taken up by 1st October 2021.
  •  Applicants must live within a reasonable distance of the University of Leeds whilst in receipt of this Studentship.

Contact details

For further information please contact the LUBS Graduate School Office or visit the studentship on the Leeds University Business School website.

e: [Email Address Removed]

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