Machine learning (ML) based systems have been suffering a lack of interpretability. To address this problem, counterfactual explanations (CEs) have been proposed. CEs are unique as they provide workable suggestions to users, in addition to explaining why a certain outcome was predicted. However, the application of CEs has been hindered by two main challenges, namely general user preferences and variable ML systems. User preferences, in particular, tend to be general rather than specific feature values. Additionally, CEs need to be customized to suit the variability of ML models, while also maintaining robustness even when these validation models change. To overcome these challenges, we propose several possible general user preferences that have been validated by user research and map them to the properties of CEs. We also introduce a new method called \uline{T}ree-based \uline{C}onditions \uline{O}ptional \uline{L}inks (T-COL), which has two optional structures and several groups of conditions for generating CEs that can be adapted to general user preferences. Meanwhile, a group of conditions lead T-COL to generate more robust CEs that have higher validity when the ML model is replaced. We compared the properties of CEs generated by T-COL experimentally under different user preferences and demonstrated that T-COL is better suited for accommodating user preferences and variable ML systems compared to baseline methods including Large Language Models.

翻译：基于机器学习的系统长期存在可解释性不足的问题。为此，反事实解释（CEs）被提出。CEs的独特之处在于，除了解释为何产生特定预测结果外，还能为用户提供可行的建议。然而，CEs的应用面临两大挑战：通用用户偏好与可变机器学习系统。用户偏好往往表现为通用特性而非具体特征值，同时CEs需具备定制化能力以适应ML模型的动态变化，并在验证模型更替时保持鲁棒性。为应对这些挑战，我们提出若干经用户研究验证的通用用户偏好类型，并将其映射至CEs的属性特征。我们进一步提出名为树形条件可选链接（T-COL）的新方法，该方法包含两种可选结构与多组条件，可生成适配通用用户偏好的CEs。其中，条件组设计使T-COL生成的CEs在ML模型替换时仍保持更高有效性。实验对比了T-COL在不同用户偏好下生成CEs的属性特征，结果表明：相较包括大语言模型在内的基线方法，T-COL更能适应个性化用户偏好与可变机器学习系统。