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.
A good illustration of the dynamic and adaptive nature of profiling is the Cross-Industry Standard Process for Data Mining (CRISP-DM).
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).
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.
This kind of profiling is used to discover the particular characteristics of a certain individual, to enable unique identification or the provision of personalized services.
A group profile can also refer to a category of people that do not form a community, but are found to share previously unknown patterns of behaviour or other characteristics (Custers 2004).
In that case the group profile describes specific behaviours or other characteristics of a category of people, like for instance women with blue eyes and red hair, or adults with relatively short arms and legs.
A profile is non-distributive when the profile does not necessarily apply to all the members of the group: the group of persons with a specific postal code have an average earning capacity of XX, or the category of persons with blue eyes has an average chance of 37% to contract a specific disease.
Note that in this case the chance of an individual to have a particular earning capacity or to contract the specific disease will depend on other factors, e.g. sex, age, background of parents, previous health, education.
[2] In the context of employment, profiles can be of use for tracking employees by monitoring their online behavior, for the detection of fraud by them, and for the deployment of human resources by pooling and ranking their skills.
(Leopold & Meints 2008)[3] Profiling can also be used to support people at work, and also for learning, by intervening in the design of adaptive hypermedia systems personalizing the interaction.
In forensic science, the possibility exists of linking different databases of cases and suspects and mining these for common patterns.
This could be used for solving existing cases or for the purpose of establishing risk profiles of potential suspects (Geradts & Sommer 2008) (Harcourt 2006).
Consumer profiles may also include behavioural attributes that assess a customer's motivation in the buyer decision process.
Well known examples of consumer profiles are Experian's Mosaic geodemographic classification of households, CACI's Acorn, and Acxiom's Personicx.
[7] Sensors monitor an individual's action and behaviours, therefore generating, collecting, analysing, processing and storing personal data.
Early examples of consumer electronics with ambient intelligence include mobile apps, augmented reality and location-based service.
[8] Profiling technologies have raised a host of ethical, legal and other issues including privacy, equality, due process, security and liability.
The people that are profiled may have to pay higher prices,[9] they could miss out on important offers or opportunities, and they may run increased risks because catering to their needs is less profitable (Lyon 2003).
This disturbs principles of due process: if a person has no access to information on the basis of which they are withheld benefits or attributed certain risks, they cannot contest the way they are being treated (Steinbock 2005).