# The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations

## TL;DR

This paper highlights the risks of unjustified counterfactual explanations in post-hoc interpretability, showing that many such explanations may not be connected to real data and are often indistinguishable from unjustified ones.

## Contribution

It critically evaluates the validity of counterfactual explanations, revealing their potential to be artifacts rather than true data-driven insights, and highlights shortcomings of current methods.

## Key findings

- High risk of unjustified counterfactuals in several datasets
- Most state-of-the-art approaches do not distinguish justified from unjustified explanations
- Unjustified explanations can be disconnected from ground-truth data

## Abstract

Post-hoc interpretability approaches have been proven to be powerful tools to generate explanations for the predictions made by a trained black-box model. However, they create the risk of having explanations that are a result of some artifacts learned by the model instead of actual knowledge from the data. This paper focuses on the case of counterfactual explanations and asks whether the generated instances can be justified, i.e. continuously connected to some ground-truth data. We evaluate the risk of generating unjustified counterfactual examples by investigating the local neighborhoods of instances whose predictions are to be explained and show that this risk is quite high for several datasets. Furthermore, we show that most state of the art approaches do not differentiate justified from unjustified counterfactual examples, leading to less useful explanations.