Jen DuBois By: Jen DuBois

Chief Talent Scientist at Manpower Advocates for AI-Driven Candidate Assessments

All organizations struggle with talent acquisition, despite sophisticated methods

Dr. Tomas Chamorro-Premuzic, Chief Talent Scientist at Manpower Group and his colleague, Data Scientist Reece Akhtar, reminded us in their recent Harvard Business Review article that there are about 6 million job seekers for 7 million jobs in the US today and that about 70% of the workforce is open to other job offers at any given time.  For fields such as Data Science, Data Engineering, Machine Learning and Advanced Analytics, this shortage of talent a propensity for turnover is a major issue for organizational competitiveness and strategy.

These two organizational psychologists assert that one largely contributing culprit to this problem is the prevalence of “backward” recruitment and hiring practices, such as unstructured interviews, that are intuition based and lead to bias in hiring.  Dr. Chamorro-Premuzic argues that if companies want to make hiring more meritocratic then they should look to technology that helps to predict, understand and, most importantly match people to the right job and organisation at scale.

His conclusion is that:

AI has the potential to significantly improve the way we identify talent as it can reduce the cost of making accurate predictions about one’s potential, while at the same time removing the bias and heuristics that so often cloud human judgement. 

We strongly agree that AI-driven platforms like QuantHub can both improve an organization’s chances of putting the right person in the right job. AI-based assessments can also help to close the Data Science and Analytics skills gap through better identification and tracking of candidate skills and reduced bias in hiring practices.

Towards the end of this article, Dr. Chamorro-Premuzic provides valuable commentary and advice on the ethical implications of using AI technology in assessing candidates.  For the full article text, click the link below (4 minute read):



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