A Predictive Model of Human Trust Evolution Over Time in AI-based Recommendations

Understanding the dynamics of human trust in AI-based recommendations is an important challenge for the design of decision support systems and human-machine teams. This study aims to advance quantitative modeling of reported trust levels evolving over successive trials across several weeks. Data from 53 participants was collected in a visual search experiment with AI system recommendations. The Feedback-based Dynamic Trust Model (F-DTM) proposed herein is based on ten predictors, focusing on variables linked to different types of delayed feedback. Six different types of machine learning regression models were compared, with the decision tree model demonstrating the best predictive performance (R² = 64.9%, RMSE = 0.72) on held-out data. Some variables were then converted into cumulative sums to capture more effectively the sequential nature of the data with a memory of past outcomes. These modifications significantly improved the performance of the new 12-variable decision tree model (R² = 69.92%, RMSE = 0.66). A subsequent analysis on this revised F-DTM model assessed the impact of eliminating variables one at a time, reaching an R² of 70.41% and an RMSE of 0.65. These findings help address the current lack of quantitative models of trust evolution in AI. However, the present cumulative sum memory approach of the F-DTM, employing supervised machine learning, may be improved on by using more complex models designed for time-series forecasting. Directions for future research include investigating temporal models, such as long short-term memory (LSTM), hidden Markov model (HMM), ARIMA or autoregressive models to predict trust evolution in AI-based recommendations.