Digital Technology Prediction of Anger, Aggression, and Violence: Recent Innovations and Methodological Considerations.

Abstract

Data derived from smartphone and wearable devices, combined with artificial intelligence/machine learning, have great potential to predict, detect, and respond to emotions and behaviors related to violence, but much remains unknown about the methodology of such an approach. We report on methodological lessons learned from two independent studies (N = 190) conducted in adults with trauma exposure (Australia), and adult couple dyads with intimate partner violence (United States), respectively, that leveraged real-world smartphone and wearable data collection to predict anger, aggression, and violence. Both studies received ethics approval to collect self-report, physiological, and GPS data. The methodological learnings of these studies showed that at-risk populations will provide valid data regarding sensitive or socially undesirable information with the goal of predicting emotions and behavior. However, there are significant participant, technical, and data challenges, as well as ethical considerations that face this nascent area of research that we synthesize for future projects. The lessons learned from these projects have important implications for prediction of anger, aggression, and violence in at-risk populations.