Statistics and Analytical Sciences
Ethics can no longer be regarded as an add-on in data science and analytics. This paper argues for the necessity of formalizing a new, practically-oriented sub-discipline of AI ethics by outlining the needs, highlighting shortcomings in current approaches, and providing a framework for ethical analytics, which is concerned with the study of the ethical issues surrounding the development, deployment, and/or dissemination of ML/AI systems and data science research, as well as the development of tools and procedures to mitigate ethical harms. While data science and machine learning are primarily concerned with data from start to finish, ethical analytics is concerned primarily with people – moral agents, the groups and societies they comprise, and the world they inhabit. Ethical analytics should be seen as complementary to the more techno-abstracted analytic disciplines, interfacing with the nuanced, ethical issues that stem from ill-defined or vague, socially-relative normative concepts. It studies the issues that arise in this holistic sociotechnical environment, and it seeks to develop concrete solutions or interventions where possible – from mathematics and algorithms to procedures and protocols.