Explainable Artificial Intelligence (XAI) has emerged as a critical area of
research aimed at enhancing the transparency and interpretability of AI
systems. Counterfactual Explanations (CFEs) offer valuable insights into the
decision-making processes of machine learning algorithms by exploring
alternative scenarios where certain factors differ. Despite the growing
popularity of CFEs in the XAI community, existing literature often overlooks
the diverse needs and objectives of users across different applications and
domains, leading to a lack of tailored explanations that adequately address the
different use cases. In this paper, we advocate for a nuanced understanding of
CFEs, recognizing the variability in desired properties based on user
objectives and target applications. We identify three primary user objectives
and explore the desired characteristics of CFEs in each case. By addressing
these differences, we aim to design more effective and tailored explanations
that meet the specific needs of users, thereby enhancing collaboration with AI
systems.

解释性人工智能 (XAI) 是一项关键研究领域，旨在增强人工智能系统的透明度和解释性。反事实解释 (CFEs) 通过探索某些因素不同的替代情景，为机器学习算法的决策过程提供有价值的洞察。本文主张细致理解 CFEs，了解用户目标和目标应用的多样化需求，以设计更有效且量身定制的解释，以满足用户的特定需求，从而提高与人工智能系统的协作。