Predicting Data that People Refuse to Disclose; How Data Mining Predictions Challenge Informational Self-Determination
Year: 2012
Type: article
Abstract: Data mining technologies are very good at predicting missing data.Datasets that are partially incomplete or incorrect can be completed or corrected by predicting characteristics.Completing or correcting data implies ascribing new characteristics to people.However, sometimes data are missing exactly because people refuse to disclose particular data, especially when these data are sensitive personal data.In general, people have a right to refuse disclosure of their personal data.This perspective on privacy, focusing on individual autonomy, is usually referred to as informational self-determination.When data mining technologies can easily deduce or predict missing data within slight margins of error, this undermines the right to informational self-determination.In fact, using predictions to fill in blanks may decrease the accuracy of the data, as predicted data may be less accurate than data provided by data subjects.As a result, paradoxically, people may be inclined to provide the personal data themselves.
Author: Bart Custers
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2767433005