Customer Fairness Perception of Artificial Intelligence – Measuring Effects in the Context of AI-based B2C E-commerce Services

Artificial Intelligence (AI) is “the ability of a machine to perform cognitive functions” (Rai et al., 2019) and becomes present among various industries and functions providing new possibilities for innovations regarding products, business models and services. It does, for example, provide new possibilities for AI-based business-to-consumer (B2C) e-commerce services such as smart mirrors (Marr, 2019) or autonomous refilling fridges (Chitkara, 2020). AI can identify patterns such as customer purchase behavior and thus predict future ones. It enables a higher personalization based on customer characteristics which can increase customer satisfaction (Saffarizadeh et al., 2017; Ameen et al., 2021). Besides these advantages of AI and AI-based services, cases of algorithmic biases, unfair AI-based decisions and discrimination against subgroups or individuals based on their sex, race or zip codes occurred (Dastin, 2018; Thorbecke, 2019; Kordzadeh et al., 2021). While AI-based e-commerce services can be personalized to the individual, also bias und subsequent unfair outcomes can occur based on data input, the algorithm itself and the human involvement (Caton et al., 2020) and harm the customer satisfaction.

Fairness of AI can be considered from various perspectives such as the technical or social perspective. While the technical perspective represents an objective view, the social perspective is more subjective representing individual perceptions. “While the literature on algorithmic fairness has long been dominated by computer science studies in an attempt to mitigate bias and discrimination from machine learning models, there has been a recent push for social science research that takes a human-centric approach” (Starke et al., 2021). The dissertation will focus on the latter by focusing on the customer fairness perception of AI-based e-commerce services. It will further identify the impact of fairness on dimensions of customer behavior and how selected technological factors impact the fairness perception.