Perspectives of Defining Algorithmic Fairness in Customer-oriented Applications: A Systematic Literature Review

Automated decision-making systems are massively engaged in different types of businesses, including customer-oriented sectors, and bring countless achievements in persuading customers with more personalized experiences. However, it was observed that the decisions made by the algorithms could bring unfairness to a person or a group of people, according to recent studies. Thus, algorithmic fairness has become a spotlight research area, and defining a concrete version of fairness notions has also become significant research. In existing literature, there are more than 21 definitions of algorithmic fairness. Many studies have shown that each notion has an incompatibility problem, and it is still necessary to make those notions more adaptable to the legal and social principles of the desired sectors. Yet, the constraints of algorithmic fairness for customer-oriented areas have not been thoroughly studied. This motivates us to work on a systematic literature review to investigate the sectors concerned about algorithmic fairness as a significant matter when using machine-based decision-making systems, what are the well-applied algorithmic fairness notions, and why they can or cannot be directly applicable to the customer-oriented sectors, what are the possible algorithmic fairness constraints for the customer-oriented sectors. By applying the standard guidelines of systematic literature review, we explored 65 prominent articles thoroughly. The findings show 43 different ways of algorithmic fairness notions in the varieties of domains. We also identified the three important perspectives to be considered for enhancing algorithmic fairness notions in the customer-oriented sectors.