Recent literature has questioned the promises of algorithms ' objectivity ( Bilić 2016;Porter 1996;Thelwall 2018 ) . For example , O'Neil ( 2016 ) has suggested , drawing from her own experience as a mathematician in finance , that an algorithm used for HR processes , such as employee recruitment , evaluation , or performance appraisals , are still impaired by racial and gender biases . For machine - learning algorithms , these biases can also stem from the data with which the algorithm was trained . An algorithm trained on historic employment data , for example , would integrate that most managers are male , thereby assuming that women are less interested in management positions . Consequently , this recruitment algorithm would not show an employment advertisement for a management position via social media to women . The advertisement would , in fact , be invisible to women ; thus , women would have no opportunity to apply . In this case , a recruitment algorithm might be actively reifying the original gender bias , based on the data with which it was trained ( Devlin 2017;O'Neil 2016 ) . Similarly , Buolamwini and Gebru ( 2018 ) showed in a recent study that facial recognition algorithms still exhibit significant racial bias , as these algorithms are less able to detect the gender of African - American women than of Caucasian women . Accordingly , Noble ( 2018 ) found that internet search algorithms privilege whiteness and discriminate against African - Americans , particularly against African - American women . The key issue here is that developers of machine - learning algorithms use data to train their algorithms . As these data might be biased according to an external reference point , the training has the great capacity to be faulty ( Barocas and Selbst 2016;Martin 2018 ) .