Black-box explanation is the problem of explaining how a machine learning model — whose internal logic is hidden to the auditor and generally complex — produces its outcomes. Current approaches for solving this problem include model explanation, outcome explanation as well as model inspection. While these techniques can be beneficial by providing interpretability, they can […] Read more

Recent technological advances rely on accurate decision support systems that have been constructed as black boxes. That is, the system’s internal logic is not available to the user, either for financial reasons or due to the complexity of system. This lack of explanation can lead to technical, ethical, and legal issues. For example, if the […] Read more