Method to Produce More Reasonable Candidate Solutions With Explanations in Intelligent Decision Support Systems

The integration of Artificial Intelligence techniques into Decision Support Systems yields effective solutions to decision problems, especially when complex scenarios are at hand. However, the use intelligent black-box models can hinder support system’s potential be fully adopted because opaque processes raise suspicions and doubts among careful makers. Moreover, appropriate comprehensible explanations may foster trustworthiness allow for reasonable adjustments or even corrections. This work proposed an approach that incorporates three reasonability aspects Systems: feasibility, rationality, plausibility. Thus, by providing makers with candidate a problem, they expected perform their tasks more effectively (i.e. decide efficiency as well efficacy). new is accompanied two proofs concept in health public security areas. Comparative results using random rational approaches, including simulation distinct user profiles, presented. achieved superior metrics regard feasibility plausibility, suggesting this proposition applied real-world applications.