Taxonomies for connecting technical components of AI-based fraud detection systems and fairness approaches

Abstract Our study investigates the inter-dependencies between the technical components of AI-based fraud detection systems and fairness approaches. Here, fairness approaches refer to any method or process to address, specify, measure or mitigate unfairness in AI systems. Despite extensive research on both AI-based fraud detection systems and AI fairness, these domains are often treated separately, leading to a fragmented understanding of both domains. This fragmentation leads to inconsistent terminologies and concepts, and fails to identify the inter-dependencies between technical components of fraud detection systems and fairness approaches. These are the research gaps we address in this study. Through a literature review of 78 papers, we synthesize existing concepts and terminologies into two structured taxonomies that allow to document these inter-dependencies. One taxonomy describes the technical components of AI fraud detection systems and another describes fairness approaches. A joint evaluation of the taxonomies reveals how technical components are impacting fairness, and how fairness considerations should govern the design and implementation of these technical components. Our findings show that four technical components (regarding class imbalance, overlapping class distributions, data quality, and adaptive learning) are intertwined with fairness approaches in fraud detection. The proposed taxonomies, and the inter-dependencies we identify, support AI practitioners and decision-makers in designing fair fraud detection systems. Additionally, the taxonomies provide structure to research investigating how technical components and technical decisions impact fairness and, vice versa, investigating how fairness approaches impact the technical design of fraud detection systems.