On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning

Understand

There is a growing concern that the recent progress made in AI, especially regarding the predictive competence of deep learning models, will be undermined by a failure to properly explain their operation and outputs.

• In response to this disquiet counterfactual explanations have become massively popular in eXplainable AI (XAI) due to their proposed computational psychological, and legal benefits.
• In contrast however, semifactuals, which are a similar way humans commonly explain their reasoning, have surprisingly received no attention.
• Most counterfactual methods address tabular rather than image data, partly due to the nondiscrete nature of the latter making good counterfactuals difficult to define.