One Explanation Does Not Fit XIL

Understand

Current machine learning models produce outstanding results in many areas but, at the same time, suffer from shortcut learning and spurious correlations.

• To address such flaws, the explanatory interactive machine learning (XIL) framework has been proposed to revise a model by employing user feedback on a model's explanation.
• This work sheds light on the explanations used within this framework.
• In particular, we investigate simultaneous model revision through multiple explanation methods.