Explainable Artificial Intelligence Models for Forecasting Divorce Risk from Dyadic Communication Patterns

Abstract

The objective of this study was to develop and interpret explainable artificial intelligence models capable of forecasting divorce risk based on dyadic communication patterns among married couples. This quantitative, observational study was conducted with legally married couples residing in Canada. Couples completed validated self-report measures assessing communication quality, emotional responsiveness, and relational characteristics, and participated in a structured dyadic interaction task designed to elicit naturally occurring conflict-related communication. Interaction transcripts were processed using natural language processing techniques to extract linguistic and interactional features reflecting positivity, negativity, contempt, emotional validation, and conversational balance. A composite divorce risk indicator was constructed from self-reported divorce proneness and separation intentions. Multiple supervised machine learning models, including regularized regression and tree-based ensemble methods, were trained and evaluated using nested cross-validation. Explainable artificial intelligence techniques were applied to identify global and local feature contributions to model predictions. Ensemble-based models demonstrated significantly higher predictive performance than linear models, achieving superior accuracy and area under the receiver operating characteristic curve. Negative communication features, particularly contempt markers and overall communication negativity, were the strongest positive predictors of divorce risk, while emotional validation and balanced turn-taking showed significant protective effects. Demographic variables contributed comparatively less to prediction once dyadic communication patterns were included. Explainability analyses revealed consistent and interpretable pathways through which specific interactional behaviors increased or reduced predicted divorce risk. The findings indicate that explainable artificial intelligence models can accurately and transparently forecast divorce risk using dyadic communication patterns, highlighting communication behaviors as central, modifiable indicators of marital instability.