With the rapid growth of generative AI in numerous applications, explainable AI (XAI) plays a crucial role in ensuring the responsible development and deployment of generative AI technologies. XAI has undergone notable advancements and widespread adoption in recent years, reflecting a concerted push to enhance the transparency, interpretability, and credibility of AI systems. Recent research emphasizes that a proficient XAI method should adhere to a set of criteria, primarily focusing on two key areas. Firstly, it should ensure the quality and fluidity of explanations, encompassing aspects like faithfulness, plausibility, completeness, and tailoring to individual needs. Secondly, the design principle of the XAI system or mechanism should cover the following factors such as reliability, resilience, the verifiability of its outputs, and the transparency of its algorithm. However, research in XAI for generative models remains relatively scarce, with little exploration into how such methods can effectively meet these criteria in that domain. In this work, we propose PXGen, a post-hoc explainable method for generative models. Given a model that needs to be explained, PXGen prepares two materials for the explanation, the Anchor set and intrinsic & extrinsic criteria. Those materials are customizable by users according to their purpose and requirements. Via the calculation of each criterion, each anchor has a set of feature values and PXGen provides examplebased explanation methods according to the feature values among all the anchors and illustrated and visualized to the users via tractable algorithms such as k-dispersion or k-center.

翻译：随着生成式人工智能在众多应用领域的快速发展，可解释人工智能（XAI）在确保生成式人工智能技术负责任地开发与部署方面发挥着至关重要的作用。近年来，XAI 取得了显著进展并得到广泛采用，这反映了业界为增强人工智能系统的透明度、可解释性与可信度而做出的协同努力。近期研究强调，一个优秀的 XAI 方法应遵循一套标准，主要聚焦于两个关键领域。首先，它应确保解释的质量与流畅性，涵盖忠实度、合理性、完整性以及针对个体需求的定制化等方面。其次，XAI 系统或机制的设计原则应涵盖可靠性、鲁棒性、输出可验证性以及算法透明度等因素。然而，针对生成模型的 XAI 研究仍相对匮乏，鲜有探索此类方法如何在该领域有效满足这些标准。在本工作中，我们提出了 PXGen，一种面向生成模型的事后可解释方法。给定一个需要解释的模型，PXGen 为解释准备两种材料：锚点集以及内在与外在标准。这些材料可由用户根据其目的和需求进行定制。通过对每条标准的计算，每个锚点获得一组特征值，PXGen 根据所有锚点间的特征值提供基于实例的解释方法，并通过诸如 k-离散度或 k-中心点等可追踪算法向用户进行说明和可视化展示。