Beyond One-Size-Fits-All: Adapting Counterfactual Explanations to User Objectives
This work addresses the lack of user-specific explanations in XAI, which is an incremental improvement for researchers and practitioners in the field.
The paper tackles the problem that counterfactual explanations in explainable AI often fail to address diverse user needs, advocating for tailored explanations based on three primary user objectives to enhance collaboration with AI systems.
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.