Ethical and Social Aspects of Data Sciences

Abstract

Data sciencea are considered to be a system of methods to improve human, society, and the multidimensional values that surround them as quickly as possible through a series of processes such as data design and collection, structural modeling, and decision making based on statistical inferences. In this process, various value conflicts emerge within individuals, social groups, and through the relationship between humans and nature. As a result, ethical anf social aspects are inevitably introduced into data sciences. A prominent example is the decision-making arena of regulatory science, where data scientists seeking to minimize consumer risk and data scientists seeking to minimize producer risk coexist adversarial and where a mutually acceptable solution must be socially defined. Apart from the issue of Inhibition Ethics, which discourages intentional misrepresentation of data-based processes, this paper discusses the ethical and socialaspects that arise when data science professionals representing various positions do what they do best.