The identification of Ethical Focus Areas: A Literature Study Into Data Mining Ethical Focus Areas

Improper use of data must be avoided, as the consequences of improper use of data can be catastrophic. In the design of information systems, ethical focus areas could help combat improper use of data. Currently, more research is available on ethical focus areas in Data Mining compared to related research fields of Data Mining, such as Decision Mining and Process Mining. For this paper, a theoretical review was conducted to identify ethical focus areas of Data Mining and their possible solutions. Seven ethical focus areas were identified focussing on privacy, collection of personal information, consent, unpredictability and inaccuracy, group profiling and biased data. Future research is needed on the ethical focus areas, to validate the possible solutions related to these ethical focus areas in the context of related research fields of Data Mining

Problem Formulation and Fairness

Formulating data science problems is an uncertain and difficult process. It requires various forms of discretionary work to translate high-level objectives or strategic goals into tractable problems, necessitating, among other things, the identification of appropriate target variables and proxies. While these choices are rarely self-evident, normative assessments of data science projects often take them for granted, even though different translations can raise profoundly different ethical concerns. Whether we consider a data science project fair often has as much to do with the formulation of the problem as any property of the resulting model. Building on six months of ethnographic fieldwork with a corporate data science team---and channeling ideas from sociology and history of science, critical data studies, and early writing on knowledge discovery in databases---we describe the complex set of actors and activities involved in problem formulation. Our research demonstrates that the specification and operationalization of the problem are always negotiated and elastic, and rarely worked out with explicit normative considerations in mind. In so doing, we show that careful accounts of everyday data science work can help us better understand how and why data science problems are posed in certain ways---and why specific formulations prevail in practice, even in the face of what might seem like normatively preferable alternatives. We conclude by discussing the implications of our findings, arguing that effective normative interventions will require attending to the practical work of problem formulation.Comment: Conference on Fairness, Accountability, and Transparency (FAT* '19), January 29-31, 2019, Atlanta, GA, US