Defining Profiling: A New Type of Knowledge?
In this first chapter a set of relevant distinctions will be made to explore old and new ways of profiling, making a first attempt to define the type of profiling that is the subject of this publication. The text explains how profiling or pattern recognition allows us to discriminate noise from information on the basis of the knowledge that is constructed, providing a sophisticated way of coping with the increasing abundance of data. The major distinctions discussed are between individual and group profiles (often combined in personalised profiling), between distributive and non-distributive group profiles and between construction and application of profiles. Having described automated profiling we will compare such machine profiling to organic and human profiling, which have been crucial competences for the survival of both human and non-human organisms. The most salient difference between organic and machine profiling may be the fact that as a citizen, consumer or employee we find ourselves in the position of being profiled, without access to the knowledge that is used to categorise and deal with us. This seems to impair our personal freedom, because we cannot adequately anticipate the actions of those that know about us what we may not know about ourselves.
KeywordsCustomer Relationship Management Data Mining Technique Data Subject Ambient Intelligence Autonomic Computing
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