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The term research impact is variously defined in academic scholarship, by national and international research funding bodies, publishers, and other relevant entities, although common definitional elements exist. Concise definitions describe the term as relating to academic research that directly and or indirectly guides policymaking processes, by enabling evidence-based decision-making and or improving understanding of a given subject area or areas [1] . Underlying this and all definitions of research impact is the fundamental assumption that the outcome(s) of university research will serve the “public good” [2, p. 1368] . 

Research on AI has gained momentum in recent years. Many scholars and practitioners increasingly highlight the dark sides of AI, particularly related to algorithm bias. This study elucidates situations in which AI-enabled analytics systems make biased decisions against customers based on gender, race, religion, age, nationality or socioeconomic status. Based on a systematic literature review, this research proposes two approaches (i.e., a priori and post-hoc) to overcome such biases in customer management. As part of a priori approach, the findings suggest scientific, application, stakeholder and assurance consistencies. With regard to the post-hoc approach, the findings recommend six steps: bias identification, review of extant findings, selection of the right variables, responsible and ethical model development, data analysis and action on insights. Overall, this study contributes to the ethical and responsible use of AI applications.