An Explainable Framework to Predict Child Sexual Abuse Awareness in People Using Supervised Machine Learning Models

Krishnaraj Chadaga, Srikanth Prabhu, Niranjana Sampathila, Rajagopala Chadaga, Muralidhar Bairy, K. S. Swathi

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Child sexual abuse (CSA) is a type of abuse in which an individual exploits a kid/adolescent sexually. CSA can happen in several places, such as schools, households, hostels, and other public spaces. However, a large number of people, including parents, do not have an awareness of this sensitive issue. Artificial intelligence (AI) and machine learning (ML) are being used in various disciplines in the modern era. Hence, supervised machine learning models have been used to predict child sexual abuse awareness in this study. The dataset contains answers provided by 3002 people regarding CSA. A questionnaire dataset obtained though crowdsourcing has been used to predict a person’s knowledge level regarding sexual abuse in children. Heterogenous ML and deep learning models have been used to make accurate predictions. To demystify the decisions made by the models, explainable artificial intelligence (XAI) techniques have also been utilized. XAI helps in making the models more interpretable, decipherable, and transparent. Four XAI techniques: Shapley additive values (SHAP), Eli5, QLattice, and local interpretable model-agnostic explanations (LIME), have been utilized to demystify the models. Among all the classifiers, the final stacked model obtained the best results with an accuracy of 94% for the test dataset. The excellent results demonstrated by the classifiers point to the use of artificial intelligence in preventing child sexual abuse by making people aware of it. The models can be used real time in facilities such as schools, hospitals, and other places to increase awareness among people regarding sexual abuse in children.

Original languageEnglish
JournalJournal of Technology in Behavioral Science
Publication statusAccepted/In press - 2023

All Science Journal Classification (ASJC) codes

  • Health(social science)
  • Applied Psychology
  • Human-Computer Interaction
  • Computer Science Applications


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