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A growing body of literature on the willingness of victims to report crimes focuses on the context in which crimes occur. Recently, a socio-ecological model has been developed from which hypotheses on the effects of social context on reporting can be derived. This study tests these hypotheses using a vignette experiment, in which 499 juveniles read a description of a violent incident and answered questions on their willingness to report to the police or to an employee of the organization they belong to (here, their school). The effects of three factors were studied: the location of the crime, the extent to which victim and offender knew each other, and whether or not the offender was part of the same organization as the victim. Results show that the willingness to contact the police is lower when the incident takes place within the organization (cf. in the public domain) and when the offender is well known (cf. vaguely known), and that there is an additional negative effect when the incident takes place within the organization and the offender also belongs to the organization. The willingness to contact an employee is higher when the offender belongs to the organization and when the incident takes place within the organization. Implications of these findings and the advantages and limitations of the vignette approach are discussed.

Prospective and retrospective indices of magnitude of change were similar between groups receiving treatment of known efficacy. Recall bias seems to be an acceptable risk in short-term follow-up studies.

Abstract-Social discrimination against certain sensitive groups within society (e.g., females, blacks, minorities) is prohibited by law in many countries. To prevent discrimination arising from the use of discriminatory data, recent data mining research has focused on methods for making classifiers learned over discriminatory data discrimination-aware. Most of these methods have been tested on standard classification datasets that have been tweaked for discrimination analysis rather than over actual discriminatory data. In this paper, we study discrimination-aware classification when applied to a realworld dataset of Statistics Netherlands, which is a census body in the Netherlands. Specifically, we consider the use of classifiers for predicting whether an individual is a crime suspect, or not, to support law enforcement and security agencies' decision making. Our results show that discrimination does exist in real world datasets and blind use of classifiers learned over such datasets can exacerbate the discrimination problem. We demonstrate that discrimination-aware classification methods can mitigate the discriminatory effects and that they lead to rational and legally acceptable decisions.