Explaining the outcomes of complex computational models

该项目旨在开发新的算法，为复杂的计算模型产生的结果自动生成解释。近年来，这些模型变得越来越精确，因此也越来越普遍。然而，深度神经网络和贝叶斯网络的推理，以及复杂的回归模型和决策树的推理往往是不清楚的，这损害了使用这些模型结果或调查系统所做决策的从业者的有效决策。通过复杂的计算模型进行清晰的决策推理的实际好处包括降低风险，提高生产力和收入，适当采用技术，包括改进从业人员的教育，以及改善最终用户的结果。通过与保健和能源领域的从业人员进行评估，将展示重大效益。

This project aims to develop new algorithms that automatically generate explanations for the results produced by complex computational models. In recent times, these models have become increasingly accurate, and hence pervasive. However, the reasoning of Deep Neural Networks and Bayesian Networks, and of complex Regression models and Decision Trees is often unclear, impairing effective decision making by practitioners who use the results of these models or investigate the decisions made by the systems. Practical benefits of clear decision making reasoning by complex computational models include reduced risk, increased productivity and revenue, appropriate adoption of technologies including improved education for practitioners, and improved outcomes for end users. Significant benefits will be demonstrated through the evaluations with practitioners in the areas of healthcare and energy.