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。

超越一刀切：将反事实解释适应用户目标

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

Trust is a key motivation in developing explainable artificial intelligence
(XAI). However, researchers attempting to measure trust in AI face numerous
challenges, such as different trust conceptualizations, simplified experimental
tasks that may not induce uncertainty as a prerequisite for trust, and the lack
of validated trust questionnaires in the context of AI. While acknowledging
these issues, we have identified a further challenge that currently seems
underappreciated - the potential distinction between trust as one construct and
\emph{distrust} as a second construct independent of trust. While there has
been long-standing academic discourse for this distinction and arguments for
both the one-dimensional and two-dimensional conceptualization of trust,
distrust seems relatively understudied in XAI. In this position paper, we not
only highlight the theoretical arguments for distrust as a distinct construct
from trust but also contextualize psychometric evidence that likewise favors a
distinction between trust and distrust. It remains to be investigated whether
the available psychometric evidence is sufficient for the existence of distrust
or whether distrust is merely a measurement artifact. Nevertheless, the XAI
community should remain receptive to considering trust and distrust for a more
comprehensive understanding of these two relevant constructs in XAI.

本文中，我们强调了不仅理论论证了疑虑作为独立于信任的第二个构想的重要性，而且从心理测量的角度证实了它们之间的区别。尽管尚未确定可用的心理测量证据是否足以支持疑虑的存在，但 XAI 社区应保持对考虑信任和疑虑以更全面的方式理解这两个相关构想的开放性。