Counterfactual explanations focus on "actionable knowledge" to help end-users understand how a machine learning outcome could be changed to a more desirable outcome. For this purpose a counterfactual explainer needs to discover input dependencies that relate to outcome changes. Identifying the minimum subset of feature changes needed to action an output change in the decision is an interesting challenge for counterfactual explainers. The DisCERN algorithm introduced in this paper is a case-based counter-factual explainer. Here counterfactuals are formed by replacing feature values from a nearest unlike neighbour (NUN) until an actionable change is observed. We show how widely adopted feature relevance-based explainers (i.e. LIME, SHAP), can inform DisCERN to identify the minimum subset of "actionable features". We demonstrate our DisCERN algorithm on five datasets in a comparative study with the widely used optimisation-based counterfactual approach DiCE. Our results demonstrate that DisCERN is an effective strategy to minimise actionable changes necessary to create good counterfactual explanations.
翻译:反事实解释侧重于“ 可诉知识”, 以帮助终端用户理解机器学习结果如何可以转换为更理想的结果。 为此, 反事实解释者需要发现与结果变化相关的投入依赖性。 确定行动决定中产出变化所需的最小特征变化子集对于反事实解释者来说是一个有趣的挑战。 本文中引入的 DisCERN 算法是一个基于案例的反事实解释器。 这里的反事实是,在观察到可采取行动的改变之前,替换与邻居(NUN)相近的特性值。 我们展示了如何广泛采用基于特征的关联性解释者( 即 LIME、 SHAP), 能够让 DisCERN 识别“ 可诉特性” 的最小子集。 我们展示了在一项比较研究中与广泛使用的基于选择的反事实方法DCEE。 我们的结果表明, DisCERN 是一种有效战略, 以最小化最小化创建良好反事实解释所需的可采取行动的变化。