关于反事实解释中樱桃选取的定义与检测
On the Definition and Detection of Cherry-Picking in Counterfactual Explanations
AI总结：
本文定义了反事实解释中的樱桃选取，并指出其检测难度大，建议优先考虑可重复性、标准化和过程约束而非事后检测。
摘要：
反事实解释被广泛用于说明输入如何改变以使模型改变其预测。对于单个实例，许多有效的反事实解释可能存在，这使得解释提供者有可能选择更符合其叙述的解释，突出有利行为并隐瞒揭示问题行为的例子。我们正式定义反事实解释中的樱桃选取，以可接受的解释空间为术语，由生成过程指定，并通过一个效用函数。然后研究外部审计员能发现此类操纵的程度有多大。考虑到三种对解释过程的访问级别：完全过程访问、部分过程访问和仅解释访问，我们显示在实践中检测极为有限。即使有完全的过程访问，樱桃选取的解释仍然难以与非樱桃选取的解释区分开来，因为有效反事实解释的多样性以及解释规范的灵活性提供了足够的自由度来掩盖故意选择。实证上，我们证明这种变化往往超过樱桃选取对标准反事实质量指标如接近性、合理性、稀疏性等的影响，使樱桃选取的解释在统计上与基线解释难以区分。我们主张应优先考虑可重复性、标准化和过程约束，而非事后检测，并为算法开发者、解释提供者和审计员提供建议。
英文摘要：
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. We formally define cherry-picking for counterfactual explanations in terms of an admissible explanation space, specified by the generation procedure, and a utility function. We then study to what extent an external auditor can detect such manipulation. Considering three levels of access to the explanation process: full procedural access, partial procedural access, and explanation-only access, we show that detection is extremely limited in practice. 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. Empirically, we demonstrate that this variability often exceeds the effect of cherry-picking on standard counterfactual quality metrics such as proximity, plausibility, and sparsity, making cherry-picked explanations statistically indistinguishable from baseline explanations. We argue that safeguards should therefore prioritise reproducibility, standardisation, and procedural constraints over post-hoc detection, and we provide recommendations for algorithm developers, explanation providers, and auditors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
