Investigating Explanations that Target Training Data
To promote transparency in black-box machine learning systems, different explanation approaches have been developed and discussed in the literature. However, training dataset information is rarely communicated in these explanations despite the utmost importance of training data to a system trained with machine learning techniques. We investigated explanations that focus on communicating training dataset information to end-users in our work. In this position paper, we discuss our prototype explanations and highlight findings from our user studies. We also discuss open questions and interesting directions for future research.
Ariful Islam Anik and Andrea Bunt. 2021. Investigating Explanations that Target Training Data. In Proceedings of the ACM IUI workshop on Transparency and Explanations in Smart Systems (TExSS). Texas, USA, 7 pages.