A number of artificial intelligence systems have been proposed to assist users in identifying the issues of algorithmic fairness and transparency. These AI systems use diverse bias detection methods from various perspectives, including exploratory cues, interpretable tools, and revealing algorithms. This study explains the design of AI systems by probing how users make sense of fairness and transparency as they are hypothetical in nature, with no specific ways for evaluation. Focusing on individual perceptions of fairness and transparency, this study examines (...) the roles of normative values in over-the-top platforms by empirically testing their effects on sensemaking processes. A mixed-method design incorporating both qualitative and quantitative approaches was used to discover user heuristics and to test the effects of such normative values on user acceptance. Collectively, a composite concept of transparent fairness emerged around user sensemaking processes and its formative roles regarding their underlying relations to perceived quality and credibility. From a sensemaking perspective, this study discusses the implications of transparent fairness in algorithmic media platforms by clarifying how and what should be done to make algorithmic media more trustable and reliable platforms. Based on the findings, a theoretical model is developed to define transparent fairness as an essential algorithmic attribute in the context of OTT platforms. (shrink)

The purpose of this work is to elaborate an empirically grounded mathematical model of the magnitude of consequences component of “moral intensity” (Jones, Academy of Management Review 16 (2),366, 1991) that can be used to evaluate different ethical situations. The model is built using the analytical hierarchy process (AHP) (Saaty, The Analytic Hierarchy Process , 1980) and empirical data from the legal profession. One contribution of our work is that it illustrates how AHP can be applied in the field of (...) ethics. Following a review of the literature, we discuss the development of the model. We then illustrate how the model can be used to rank-order three well-known ethical reasoning cases in terms of the magnitude of consequences. The work concludes with implications for theory, practice, and future research. Specifically we discuss how this work extends the previous work by Collins ( Journal of Business Ethics 8 , 1, 1989) regarding the nature of harm variable. We also discuss the contribution this work makes in the development of ethical scenarios used to test hypotheses in the field of business ethics. Finally, we discuss how the model can be used for after-action review, contribute to organizational learning, train employees in ethical reasoning, and aid in the design and development of decision support systems that support ethical reasoning. (shrink)

The purpose of this work is to elaborate an empirically grounded mathematical model of the magnitude of consequences component of "moral intensity", 366, 1991) that can be used to evaluate different ethical situations. The model is built using the analytical hierarchy process and empirical data from the legal profession. One contribution of our work is that it illustrates how AHP can be applied in the field of ethics. Following a review of the literature, we discuss the development of the model. (...) We then illustrate how the model can be used to rank-order three well-known ethical reasoning cases in terms of the magnitude of consequences. The work concludes with implications for theory, practice, and future research. Specifically we discuss how this work extends the previous work by Collins regarding the nature harm variable. We also discuss the contribution this work makes in the development of ethical scenarios used to test hypotheses in the field of business ethics. Finally, we discuss how the model can be used for after-action review, contribute to organizational learning, train employees in ethical reasoning, and aid in the design and development of decision support systems that support ethical reasoning. (shrink)