Interesting research comes from University College Cork. The study's subject is an old HR-Recruitment topic of how to minimize bias in hiring and how to identify the best candidates using analytics and data. The authors use two decision-making techniques, AHP and Topsis (which were new to me as well), to create an analytical model that will make the final decision on which candidate should be hired. There are three steps in the process:
1. HR Inputs - The responsible HR defines the roles and the criteria needed for ranking candidates, such as the number of positions, priority, required skills, budget constraints, and so on.
2. Interview Schedule and Ratings - The interviewer (hiring manager or team member) rates the candidates based on their technical and soft skills defined in the previous stage.
3. Decision-making and optimization using prescriptive analytics - In the third stage, the hiring manager receives the final results from the system stating which candidates need to be hired. The model uses machine learning algorithms to analyze a range of factors, including education, experience, skills, and personality traits, to predict which candidates are most likely to succeed in a given role.
This approach may seem progressive, but it is important to gradually introduce systems like this to show their effectiveness in making final decisions. The researchers suggest that this method could help reduce blind spots and inaccuracies in the decision-making process, and they may be correct.
Here you can read more about it:
https://www.researchgate.net/publication/368242108_A_prescriptive_analytics_approach_to_employee_selection
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