Beyond One-Size-Fits-All: Adapting Counterfactual Explanations to User Objectives

TL;DR

This work argues that counterfactual explanations (CFEs) cannot be one-size-fits-all due to diverse user goals. It proposes a framework that distinguishes three user objectives and analyzes how actionability and plausibility should be prioritized for each. The paper articulates precise requirements for CFEs in outcome fulfillment, system investigation, and vulnerability detection, highlighting trade-offs between realism and informative contrast. By advocating contextualized design, the authors aim to improve the usefulness and trustworthiness of CFEs across domains and applications.

Abstract

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.