The development of automated preference elicitation tools has seen increased interest among researchers in recent years due to a growing interest in such diverse problems as development of user-adaptive software and greater involvement of patients in medical decision making. These tools not only must facilitate the elicitation of reliable information without overly fatiguing the interviewee but must also take into account changes in preferences. In this paper, we introduce two complementary indicators for detecting change in preference which can be used depending on the granularity of observed information. The first indicator exploits conflicts between the current model and the observed preference by using intervals mapped to gamble questions as guides in observing changes in risk attitudes. The second indicator relies on answers to gamble questions, and uses Chebyshev’s inequality to infer the user’s risk attitude. The model adapts to the change in preference by relearning whenever an indicator exceeds some preset threshold. We implemented our utility model using knowledge-based artificial neural networks that encode assumptions about a decision maker’s preferences. This allows us to learn a decision maker’s utility function from a relatively small set of answers to gamble questions thereby minimizing elicitation cost. Results of our experiments on a simulated change of real patient preference data suggest significant gain in performance when the utility model adapts to change in preference.