This paper extends a lot of the ideas in "A Unified Approach to Interpreting Model Predictions" (reference 19). The term unified is also used in the OP paper.

This paper extends a lot of the ideas in "A Unified Approach to Interpreting Model Predictions" (reference 19). The term unified is also used in the OP paper. That reference paper is unifying in the sense that it could tie together a lot of frameworks such as LIME, QII, and others. The paper in OP is unifying in the sense of the explanation games/game formulations (while also referencing QII). The perspective of using contrastive explanations is very intuitive and becoming more common in different aspects of ML. My only wish is that I could try this FAE stuff myself. The authors of SHAP have a pretty immediately accessible python library. Could not find the one for this novel framework. It is not that I doubt the results. My point is a lot of good ideas like the ones presented in the paper often don't take off unless the authors also give easily pip-able libraries. edit: I found the code[1], but it is not an easily conda-able library. [1]: https://github.com/fiddler-labs/the-explanation-game-supplemental https://github.com/fiddler-labs/the-explanation-game-supplem...