In information science, profiling refers to the process of construction and application of user profiles generated by computerized data analysis. This is the use of algorithms or other mathematical techniques that allow the discovery of 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 in this sense is not just about the construction of profiles, but also concerns the application of group profiles to individuals, e. g., in the cases of credit scoring, price discrimination, or identification of security risks (Hildebrandt & Gutwirth 2008) (Elmer 2004). Profiling is being used in fraud prevention, ambient intelligence, consumer analytics, and surveillance. Statistical methods of profiling include Knowledge Discovery in Databases (KDD).
The profiling process The technical process of profiling can be separated in several steps:
Preliminary grounding: The profiling process starts with a specification of the applicable problem domain and the identification of the goals of analysis. Data collection: The target dataset or database for analysis is formed by selecting the relevant data in the light of existing domain knowledge and data understanding. Data preparation: The data are preprocessed for removing noise and reducing complexity by eliminating attributes. Data mining: The data are analysed with the algorithm or heuristics developed to suit the data, model and goals. Interpretation: The mined patterns are evaluated on their relevance and validity by specialists and/or professionals in the application domain (e.g. excluding spurious correlations). Application: The constructed profiles are applied, e.g. to categories of persons, to test and fine-tune the algorithms. Institutional decision: The institution decides what actions or policies to apply to groups or individuals whose data match a relevant profile. Data collection, preparation and mining all belong to the phase in which the profile is under construction. However, profiling also refers to the application of profiles, meaning the usage of profiles for the identification or categorization of groups or individual persons. As can be seen in step six (application), the process is circular. There is a feedback loop between the construction and the application of profiles. The interpretation of profiles can lead to the reiterant – possibly real-time – fine-tuning of specific previous steps in the profiling process. The application of profiles to people whose data were not used to construct the profile is based on data matching, which provides new data that allows for further adjustments. The process of profiling is both dynamic and adaptive. A good illustration of the dynamic and adaptive nature of profiling is the Cross-Industry Standard Process for Data Mining (CRISP-DM).
Types of profiling practices In order to clarify the nature of profiling technologies, some crucial distinctions have to be made between different types of profiling practices, apart from the distinction between the construction and the application of profiles. The main distinctions are those between bottom-up and top-down profiling (or supervised and unsupervised learning), and between individual and group profiles.
Supervised and unsupervised learning Profiles can be classified according to the way they have been generated (Fayyad, Piatetsky-Shapiro & Smyth 1996) (Zarsky & 2002-3). On the one hand, profiles can be generated by testing a hypothesized correlation. This is called top-down profiling or supervised learning. This is similar to the methodology of traditional scientific research in that it starts with a hypothesis and consists of testing its validity. The result of this type of profiling is the verification or refutation of the hypothesis. One could also speak of deductive profiling. On the other hand, profiles can be generated by exploring a data base, using the data mining process to detect patterns in the data base that were not previously hypothesized. In a way, this is a matter of generating hypothesis: finding correlations one did not expect or even think of. Once the patterns have been mined, they will enter the loop – described above – and will be tested with the use of new data. This is called unsupervised learning. Two things are important with regard to this distinction. First, unsupervised learning algorithms seem to allow the construction of a new type of knowledge, not based on hypothesis developed by a researcher and not based on causal or motivational relations but exclusively based on stochastical correlations. Second, unsupervised learning algorithms thus seem to allow for an inductive type of knowledge construction that does not require theoretical justification or causal explanation (Custers 2004). Some authors claim that if the application of profiles based on computerized stochastical pattern recognition 'works', i.e. allows for reliable predictions of future behaviours, the theoretical or causal explanation of these patterns does not matter anymore (Anderson 2008). However, the idea that 'blind' algorithms provide reliable information does not imply that the information is neutral. In the process of collecting and aggregating data into a database (the first three steps of the process of profile construction), translations are made from real-life events to machine-readable data. These data are then prepared and cleansed to allow for initial computability. Potential bias will have to be located at these points, as well as in the choice of algorithms that are developed. It is not possible to mine a database for all possible linear and non-linear correlations, meaning that the mathematical techniques developed to search for patterns will be determinate of the patterns that can be found. In the case of machine profiling, potential bias is not informed by common sense prejudice or what psychologists call stereotyping, but by the computer techniques employed in the initial steps of the process. These techniques are mostly invisible for those to whom profiles are applied (because their data match the relevant group profiles).
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