  Algorithmic processes are increasingly employed to perform managerial
decision making, especially after the tremendous success in Artificial
Intelligence (AI). This paradigm shift is occurring because these sophisticated
AI techniques are guaranteeing the optimality of performance metrics. However,
this adoption is currently under scrutiny due to various concerns such as
fairness, and how does the fairness of an AI algorithm affects user's trust is
much legitimate to pursue. In this regard, we aim to understand the
relationship between induced algorithmic fairness and its perception in humans.
In particular, we are interested in whether these two are positively correlated
and reflect substantive fairness. Furthermore, we also study how does induced
algorithmic fairness affects user trust in algorithmic decision making. To
understand this, we perform a user study to simulate candidate shortlisting by
introduced (manipulating mathematical) fairness in a human resource recruitment
setting. Our experimental results demonstrate that different levels of
introduced fairness are positively related to human perception of fairness, and
simultaneously it is also positively related to user trust in algorithmic
decision making. Interestingly, we also found that users are more sensitive to
the higher levels of introduced fairness than the lower levels of introduced
fairness. Besides, we summarize the theoretical and practical implications of
this research with a discussion on perception of fairness.
