Discrimination, Bias, Fairness, and Trustworthy AI

Featured Application To understand the multiple definitions available for the variables "Discrimination", "Bias", "Fairness", and "Trustworthy AI" in the context of the social impact of algorithmic decision-making systems (ADMS), pursuing to reach consensus as working variables for the referred context. In this study, we analyze "Discrimination", "Bias", "Fairness", and "Trustworthiness" as working variables in the context of the social impact of AI. It has been identified that there exists a set of specialized variables, such as security, privacy, responsibility, etc., that are used to operationalize the principles in the Principled AI International Framework. These variables are defined in such a way that they contribute to others of more general scope, for example, the ones studied in this study, in what appears to be a generalization-specialization relationship. Our aim in this study is to comprehend how we can use available notions of bias, discrimination, fairness, and other related variables that will be assured during the software project's lifecycle (security, privacy, responsibility, etc.) when developing trustworthy algorithmic decision-making systems (ADMS). Bias, discrimination, and fairness are mainly approached with an operational interest by the Principled AI International Framework, so we included sources from outside the framework to complement (from a conceptual standpoint) their study and their relationship with each other.