A new research project which began late last year will examine how to use data and artificial intelligence to remove bias from the recruitment process.

Marie Curie PhD fellow and data expert Kolawole Adebayo will develop Laibre with a budget of €259,550 over the next three years. It aims to use AI to end discrimination in hiring, thus attracting more diverse talent.

“Hiring bias occurs when candidates feel excluded or are considered positively or negatively because of certain characterisations which have nothing to do with their ability. Hiring bias can lead to undue discrimination of quality candidates from the disadvantaged or minority groups such as women, people of colour, and those in the LGBTIQ community,” Adebayo said.

He explained how he will use advanced natural language processing techniques to eliminate bias across different human resources workflows.

It will implement AI models that can understand the contents of HR documents to extract and remove information that can lead to unconscious bias and discrimination at the attraction and selection phases of hiring.

“For example at the job advertisement phase, Laibre will develop a domain adapted language model to aid the rewriting of inclusive job adverts; while at the candidate review phase, Laibre will implement a context-aware information extraction model to identify and remove protected attributes such as gender, ethnicity, etc. The project will implement models that learn intrinsic candidate suitability patterns from anonymised data for automated candidates review and selection towards skills-based hiring.”

Adebayo’s research will be supervised by Prof Brian Davis of the School of Computing at DCU and the SFI ADAPT Centre.

Speaking about the potential impact of the research, Davis said, “Considering that recruiting is an economic gateway for many people to access job opportunities and earn income to support themselves and their families, the process needs to be fair and unbiased and factors like gender, ethnicity, or sexual orientation should not determine who gets hired or not. Moreover, the issue can be viewed not just from the moral angle but also an economic one.”

“Tackling bias in hiring will potentially reduce ‘bad hires’ while promoting diversity in workplaces which aside [from] addressing an important equality issue also translates to more innovation, productivity, and profitability for employers,” Davis added.

Adebayo will also work with industry partner Datalive Analytics as well as Trinity College Dublin’s Learnovate Centre, which is focused on industry-led technology research. Most recently, it has played host to the researchers working on the Alpaca children’s literacy tool.

A spokesperson from Datalive Analytics said that the company was “building novel solutions for the future of hiring – including elimination of bias from hiring and identification of skill gaps to assist rapid learning and development of employees and jobseekers.”

“We are developing a live-link supply and demand for the labour market of the future where diversity and flexibility of the workforce and skill traceability will be key to employability.”

“The Laibre project will help transform recruitment into an efficient, analytics-based, unbiased and de-risked ecosystem and for us as a company, it is coming at no better time than this post-pandemic period where fair hiring practices can help put the right candidates to work.”

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