By Bart Custers, Toon Calders, Bart Schermer, Tal Zarsky
Vast quantities of knowledge are these days gathered, saved and processed, so that it will help in making quite a few administrative and governmental judgements. those cutting edge steps significantly enhance the rate, effectiveness and caliber of choices. Analyses are more and more played through facts mining and profiling applied sciences that statistically and instantly be certain styles and tendencies. notwithstanding, while such practices result in undesirable or unjustified decisions, they might bring about unacceptable types of discrimination.
Processing colossal quantities of information could lead to events during which facts controllers understand a few of the features, behaviors and whereabouts of individuals. occasionally, analysts may perhaps comprehend extra approximately contributors than those participants find out about themselves. Judging humans by means of their electronic identities sheds a distinct mild on our perspectives of privateness and knowledge safety.
This publication discusses discrimination and privateness concerns with regards to info mining and profiling practices. It presents technological and regulatory suggestions, to difficulties which come up in those cutting edge contexts. The ebook explains that universal measures for mitigating privateness and discrimination, reminiscent of entry controls and anonymity, fail to correctly unravel privateness and discrimination matters. hence, new options, targeting know-how layout, transparency and responsibility are known as for and set forth.
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Additional resources for Discrimination and privacy in the information society : data mining and profiling in large databases
These tools, along with other tools for data structuring and analysis, are extremely important and it would be very difficult for an information society like ours if they would not be available. To stress this point we will provide here some major advantages of profiling. The advantages of profiling usually depend on the context in which they are used. Nevertheless, some advantages may hold for many or most contexts. At times group profiles may be advantageous compared to individual profiles. Sometimes profiling, whether it is individual profiling or group profiling, may be advantageous compared to no profiling at all.
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17 A weighing of the distances is also possible, if particular attributes are considered more important. It should be mentioned that the number of dimensions n included in the clustering method might need to be limited for several reasons. 18 But high-dimensional spaces also make it difficult to interpret the results, since it may be hard to apply intuition. 19 The calculation of distance scores usually requires several assumptions. For instance, when the data concerns persons, it is assumed that persons of the same type are close together in the data space.