The growing complexity of AI systems has intensified the need for transparency through Explainable AI (XAI). Counterfactual explanations (CFs) offer actionable "what-if" scenarios on three levels: Local CFs providing instance-specific insights, Global CFs addressing broader trends, and Group-wise CFs (GWCFs) striking a balance and revealing patterns within cohesive groups. Despite the availability of methods for each granularity level, the field lacks a unified method that integrates these complementary approaches. We address this limitation by proposing a gradient-based optimization method for differentiable models that generates Local, Global, and Group-wise Counterfactual Explanations in a unified manner. We especially enhance GWCF generation by combining instance grouping and counterfactual generation into a single efficient process, replacing traditional two-step methods. Moreover, to ensure trustworthiness, we innovatively introduce the integration of plausibility criteria into the GWCF domain, making explanations both valid and realistic. Our results demonstrate the method's effectiveness in balancing validity, proximity, and plausibility while optimizing group granularity, with practical utility validated through practical use cases.

翻译：人工智能系统日益复杂，加剧了对可解释人工智能（XAI）透明性的需求。反事实解释（CFs）可在三个层面提供可操作的“假设”场景：局部CFs提供实例级洞见，全局CFs应对宏观趋势，而群组CFs（GWCFs）则实现了平衡，揭示 cohesive 群体内部的模式。尽管针对每个粒度级别均有相应方法，但该领域尚缺乏一种能够整合这些互补方法的统一方法。为弥补此局限，我们提出一种针对可微模型的基于梯度的优化方法，该方法能以统一方式生成局部、全局及群组反事实解释。我们特别通过将实例分组与反事实生成整合为单一高效流程来增强GWCF生成，取代传统的两步法。此外，为确保可信度，我们创新地将可信度准则引入GWCF领域，使解释兼具有效性与真实性。实验结果证明了该方法在优化群组粒度的同时平衡有效性、接近性与可信度的能力，并通过实际用例验证了其实用价值。