A1 Journal article – refereed
Counteracting deceptive responding in the Finnish Investigative Instrument of Child Sexual Abuse (FICSA)
List of Authors: Alessandro Tadei, Juulia Haajanen, Johan Pensar, Pekka Santtila, Jan Antfolk
Publisher: ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
Publication year: 2020
Journal: Journal of Sexual Aggression
Journal name in source: JOURNAL OF SEXUAL AGGRESSION
Journal acronym: J SEX AGGRESS
Number of pages: 12
ISSN: 1355-2600
eISSN: 1742-6545
DOI: http://dx.doi.org/10.1080/13552600.2020.1846802
Abstract
Unfounded child sexual abuse (CSA) allegations take investigative resources from real cases and have detrimental consequences for the people involved. The Finnish Investigative Instrument of Child Sexual Abuse (FICSA) supports investigators by estimating the probability of a CSA allegation being true based on the child's background information. In the current study, we aimed at making FICSA resistant to deception. Two gender-specific questionnaires with FICSA questions and additional "trap" questions were constructed. The trap questions were designed to seem statistically related to CSA although they were not. Combining the answers of 278 real victims and 275 16-year-old students, instructed to simulate being CSA victims, we built a Naive Bayes classifier that successfully separated the two groups (AUC = 0.91 for boys and 0.92 for girls). By identifying false allegations early in the investigation, authorities' resources can be directed towards allegations that are more likely true, effectively helping actual CSA victims.
Unfounded child sexual abuse (CSA) allegations take investigative resources from real cases and have detrimental consequences for the people involved. The Finnish Investigative Instrument of Child Sexual Abuse (FICSA) supports investigators by estimating the probability of a CSA allegation being true based on the child's background information. In the current study, we aimed at making FICSA resistant to deception. Two gender-specific questionnaires with FICSA questions and additional "trap" questions were constructed. The trap questions were designed to seem statistically related to CSA although they were not. Combining the answers of 278 real victims and 275 16-year-old students, instructed to simulate being CSA victims, we built a Naive Bayes classifier that successfully separated the two groups (AUC = 0.91 for boys and 0.92 for girls). By identifying false allegations early in the investigation, authorities' resources can be directed towards allegations that are more likely true, effectively helping actual CSA victims.
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