As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
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.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.