A Bayesian Decision-Support Tool for Child Sexual Abuse Assessment and Investigation

Abstract

In assessments of child sexual abuse (CSA) allegations, informative background information is often overlooked or not used properly. We therefore created and tested an instrument that uses accessible background information to calculate the probability of a child being a CSA victim that can be used as a starting point in the following investigation. Studying 903 demographic and socioeconomic variables from over 11,000 Finnish children, we identified 42 features related to CSA. Using Bayesian logic to calculate the probability of abuse, our instrument-the Finnish Investigative Instrument of Child Sexual Abuse (FICSA)-has two separate profiles for boys and girls. A cross-validation procedure suggested excellent diagnostic utility (area under the curve [AUC] = 0.97 for boys and AUC = 0.88 for girls). We conclude that the presented method can be useful in forensic assessments of CSA allegations by adding a reliable statistical approach to considering background information, and to support clinical decision making and guide investigative efforts.

The Police Use of Background Information Related to Alleged Victims in Mock Evaluations of Child Sexual Abuse

“…To measure how correct participants were in estimating CSA probability, we compared their values with the ones we obtained by running FICSA on the four scenarios and that we considered correct. Even if it has been demonstrated that FICSA has excellent diagnostic validity (AUC = 0.88 for girls and 0.97 for boys; Tadei et al 2017), there is still the possibility for a scenario to be among the false positives or false negatives. In this case, the police officers' estimations might be right and the FICSA's ones, that here we considered correct, might be wrong.…”

“…Seen from a decision-making point of view, background information can be used to provide evidence that can justify prosecution, by estimating the probability of an allegation being true. Using a naïve Bayes classifier, Tadei et al (-2017) identified 42 pieces of background information that predicted the CSA probability with a high level of accuracy for both female and male alleged victims (AUC = 0.88 for girls and 0.97 for boys, based on an equal folds cross validation procedure, repeated 100 times). This classifier, called FICSA (Finnish Investigative Instrument of Child Sexual Abuse), was trained on data from more than 11,000 children, either 12 or 15 years old, and 903 background variables (Ellonen et al 2013).…”

“…When applied to forensic evaluations, it has been demonstrated, however, that not only the creation of a classifier (Tadei et al 2017;Zawoad and Hasan 2015) but also the interpretation of its results (Fenton and Neil 2012;Koehler 2011;Tadei et al 2018) can be problematic. When in contrast with their own opinion, expert witnesses and legal professionals tend not to consider the results provided by a statistical tool built on actuarial data (Tadei et al 2018).…”

“…We defined a variable as CSA-unrelated when the chi-squared test between the variable and the presence or absence of CSA experiences was statistically non-significant and the chisquared test value was close to zero. We considered a variable CSA-related when the chi-squared test between the variable and CSA was statistically significant and the variable was used as a feature in FICSA (Tadei et al 2017).…”

The prevalence of unfounded suspicions of child sexual abuse in Finland

“…The authors concluded that there were some regional differences and that prevalence estimates vary largely due to differences in both research methodology and local definitions of CSA (see also Pereda, Guilera, Forns, & Gómez-Benito, 2009). Accurate prevalence estimates are crucial for decision making in CSA cases (e.g., Faust, Bridges and Ahern, 2009;Tadei, Pensar, Corander, Finnilä, Santtila, & Antfolk, 2017).…”