  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.
