Understanding Relations Between Perception of Fairness and Trust in Algorithmic Decision Making (2109.14345v1)

Abstract: Algorithmic processes are increasingly employed to perform managerial decision making, especially after the tremendous success in 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.