Transparency in Data Mining: From Theory to Practice

Abstract

A broad variety of governmental initiatives are striving to use advanced computerized processes to predict human behavior. This is especially true when the behavioral trends sought generate substantial risks or are difficult to enforce. Data mining applications are the technological tools which make governmental prediction possible. The growing use of predictive practices premised upon the analysis of personal information and powered by data mining, has generated a flurry of negative reactions and responses. A central concern often voiced in this context is the lack of transparency these processes entail. Although echoed across the policy, legal and academic debate, the nature of transparency in this context is unclear and calls for a rigorous analysis. Transparency might pertain to different segments of the data mining and prediction process. This chapter makes initial steps in illuminating the true meaning of transparency in this specific context and provides tools for further examining this issue.

This chapter begins by briefly describing and explaining the practices of data mining, when used to predict future human conduct on the basis of previously collected personal information. It then moves to address the flow of information generated in the prediction process. In doing so, it introduces a helpful taxonomy regarding four distinct segments within the prediction process. Each segment presents unique transparency-related challenges. Thereafter, the chapter provides a brief theoretical analysis seeking the foundations for transparency requirements. The analysis addresses transparency as a tool to enhance government efficiency, facilitate crowdsourcing and promote autonomy. Finally, the chapter concludes by bringing the findings of the two previous sections together. It explains at which contexts the arguments for transparency are strongest, and draws out the implications of these conclusions.