Naar een professionelere selectiepsychologie: Beslissen op basis van algoritmes

Using simple algorithms to combine information (mechanical prediction) results in more accurate decisions than combining such information ‘in the head’ (holistic prediction). Yet, many decision makers are still averse to using algorithms in hiring and admissions decisions. In this article, we give an overview of dissertation research on how to overcome algorithm aversion in hiring and admissions decisions. The f indings show that retaining decision makers’ autonomy in mechanical prediction encourages algorithm use, and often results in more accurate selection decisions than holistic prediction. Furthermore, training decision makers in mechanical prediction increased algorithm use. We discuss the practical implications of this dissertation and present steps that practitioners and academics can take to encourage algorithm use and improve decision-making.