On the Definition and Detection of Cherry-Picking in Counterfactual Explanations

Counterfactual explanations are widely used to communicate how inputs must change for a model to alter its prediction. For a single instance, many valid counterfactuals can exist, which leaves open the possibility for an explanation provider to cherry-pick explanations that better suit a narrative of their choice, highlighting favourable behaviour and withholding examples that reveal problematic behaviour. The authors formally define cherry-picking for counterfactual explanations. They then study to what extent an external auditor can detect such manipulation. Even with full procedural access, cherry-picked explanations can remain difficult to distinguish from non-cherry-picked explanations, because the multiplicity of valid counterfactuals and flexibility in the explanation specification provide sufficient degrees of freedom to mask deliberate selection. They demonstrate empirically that this variability often exceeds the effect of cherry-picking on standard counterfactual quality metrics. They therefore argue that safeguards should prioritise ex ante standardisation over the use of metrics ex post. Without these safeguards, explanations can become a tool for obfuscation rather than transparency.