Profiling Practices

Profiling Practices

Profiling (Information science) refers to the whole process of construction and application of profiles generated by computerized profiling technologies. What characterizes profiling technologies is the use of algorithms or other mathematical techniques that allow one to discover patterns or correlations in large quantities of data, aggregated in databases. When these patterns or correlations are used to identify or represent people they can be called profiles. Other than a discussion of profiling technologies or population profiling the notion of profiling practices is not just about the construction of profiles, but also concerns the application of group profiles to individuals, e.g. in the case of credit scoring, price discrimination, or identification of security risks (Hildebrandt & Gutwirth 2008) (Elmer 2004).

Profiling is not simply a matter of computerized pattern recognition; it enables refined price-discrimination, targeted servicing, detection of fraud, and extensive social sorting. Real-time machine profiling constitutes the precondition for emerging socio-technical infrastructures envisioned by advocates of ambient intelligence, Autonomic Computing (Kephart & Chess 2003) and ubiquitous computing (Weiser 1991).

One of the most challenging problems of the information society is dealing with the increasing data overload. With the digitizing of all sorts of content as well as the improvement and drop in cost of recording technologies, the amount of available information has become enormous and is increasing exponentially. It has thus become important for companies, governments, and individuals to be able to discriminate information from noise, detecting those data that are useful or interesting. The development of profiling technologies must be seen against this background. These technologies are thought to efficiently collect and analyse data in order to find or test knowledge in the form of statistical patterns between data. This process is called Knowledge Discovery in Databases (KDD) (Fayyad, Piatetsky-Shapiro & Smyth 1996), which provides the profiler with sets of correlated data that are used as "profiles".