“The Human Must Remain the Central Focus”: Subjective Fairness Perceptions in Automated Decision-Making

The increasing use of algorithms in allocating resources and services in both private industry and public administration has sparked discussions about their consequences for inequality and fairness in contemporary societies. Previous research has shown that the use of automated decision-making (ADM) tools in high-stakes scenarios like the legal justice system might lead to adverse societal outcomes, such as systematic discrimination. Scholars have since proposed a variety of metrics to counteract and mitigate biases in ADM processes. While these metrics focus on technical fairness notions, they do not consider how members of the public, as most affected subjects by algorithmic decisions, perceive fairness in ADM. To shed light on subjective fairness perceptions of individuals, this study analyzes individuals’ answers to open-ended fairness questions about hypothetical ADM scenarios that were embedded in the German Internet Panel (Wave 54, July 2021), a probability-based longitudinal online survey. Respondents evaluated the fairness of vignettes describing the use of ADM tools across different contexts. Subsequently, they explained their fairness evaluation providing a textual answer. Using qualitative content analysis, we inductively coded those answers (N = 3697). Based on their individual understanding of fairness, respondents addressed a wide range of aspects related to fairness in ADM which is reflected in the 23 codes we identified. We subsumed those codes under four overarching themes: Human elements in decision-making, Shortcomings of the data, Social impact of AI, and Properties of AI. Our codes and themes provide a valuable resource for understanding which factors influence public fairness perceptions about ADM.