Making Explainable Recommendations Within an Intelligent Information System

摘要

The development of methods that can generate compelling and accurate explanations of machine learning models and their predictions would mark a major advance in the state of the art by enabling developers and end users to detect model shortcomings, understand why predictions are made, and enable rich human-automation dialog. In our ongoing research, we seek to make contributions in three areas. First, our system utilizes abductive inference to produce more compelling and accurate explanations than prior methods. Second, our approach fully integrates explanations as actionable tools within a recommendation system. Third, accumulated feedback and abductive reasoning support the discovery of new features that hold the potential for improving subsequent rounds of machine learning.