剖析图像AI可解释性技术的现状与挑战，助力提升AI可信度。
The State of Post-Hoc Local XAI Techniques for Image Processing: Challenges and Motivations
- 系统梳理后处理类XAI在图像领域的研究动机与方法
- 指出当前XAI面临透明性不足、评估标准缺失等核心难题
- 适合关注AI可信性、模型解释性的研究人员参考
随着复杂AI系统日益融入日常生活，其固有的黑箱特性成为亟待解决的关键问题。为提升效率而发展技术的同时，必须重视增强AI系统的整体可信性。本文聚焦可解释人工智能（XAI）领域，探讨其研究动机、主流方法、面临的深层挑战及未解难题。文章系统分析了图像处理中后处理类XAI技术的现状，指出其在可解释性、一致性与评估体系上的局限，并提出若干值得深入探索的方向，旨在推动XAI研究的积极发展。
原文摘要 · Abstract (English)
As complex AI systems further prove to be an integral part of our lives, a persistent and critical problem is the underlying black-box nature of such products and systems. In pursuit of productivity enhancements, one must not forget the need for various technology to boost the overall trustworthiness of such AI systems. One example, which is studied extensively in this work, is the domain of Explainable Artificial Intelligence (XAI). Research works in this scope are centred around the objective of making AI systems more transparent and interpretable, to further boost reliability and trust in using them. In this work, we discuss the various motivation for XAI and its approaches, the underlying challenges that XAI faces, and some open problems that we believe deserve further efforts to look into. We also provide a brief discussion of various XAI approaches for image processing, and finally discuss some future directions, to hopefully express and motivate the positive development of the XAI research space.
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