In eXplainable Artificial Intelligence (XAI), counterfactual explanations are known to give simple, short, and comprehensible justifications for complex model decisions. However, we are yet to see more applied studies in which they are applied in real-world cases. To fill this gap, this study focuses on showing how counterfactuals are applied to employability-related problems which involve complex machine learning algorithms. For these use cases, we use real data obtained from a public Belgian employment institution (VDAB). The use cases presented go beyond the mere application of counterfactuals as explanations, showing how they can enhance decision support, comply with legal requirements, guide controlled changes, and analyze novel insights.

翻译：在可解释人工智能（XAI）领域，反事实解释被认为能为复杂模型决策提供简洁、简短且易于理解的说明。然而，目前尚缺乏将反事实解释应用于实际案例的研究。为填补这一空白，本研究聚焦于展示反事实解释如何应用于涉及复杂机器学习算法的就业能力相关问题。这些用例基于我们从比利时公共就业机构（VDAB）获取的真实数据。所呈现的用例超越了单纯将反事实作为解释的应用，展示了它们如何增强决策支持、满足法律要求、引导受控变更并分析新颖见解。