Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
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DOI：
10.1109/access.2018.2870052
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发表时间：
2018-01-01
期刊：
影响因子：
3.9
通讯作者：
Berrada, Mohammed
中科院分区：
文献类型：
--
作者：
Adadi, Amina;Berrada, Mohammed
在第四次工业革命的黎明，我们正在目睹人工智能（AI）在我们的日常生活中快速而广泛的采用，这有助于加速向算法社会的转变。然而，即使有了这样前所未有的进步，使用基于人工智能的系统的一个关键障碍是它们往往缺乏透明度。事实上，这些系统的黑箱性质允许强大的预测，但它不能直接解释。这个问题引发了关于可解释AI（XAI）的新辩论。一个研究领域为提高基于AI的系统的信任和透明度提供了巨大的希望。它被认为是人工智能在不受干扰的情况下继续稳步进展的必要条件。这项调查为感兴趣的研究人员和从业人员提供了一个切入点，以了解与XAI相关的年轻和快速增长的研究机构的关键方面。通过文献的透镜，我们回顾了现有的方法关于这个话题，讨论了其领域的趋势，并提出了主要的研究轨迹。
At the dawn of the fourth industrial revolution, we are witnessing a fast and widespread adoption of artificial intelligence (AI) in our daily life, which contributes to accelerating the shift towards a more algorithmic society. However, even with such unprecedented advancements, a key impediment to the use of AI-based systems is that they often lack transparency. Indeed, the black-box nature of these systems allows powerful predictions, but it cannot be directly explained. This issue has triggered a new debate on explainable AI (XAI). A research field holds substantial promise for improving trust and transparency of AI-based systems. It is recognized as the sine qua non for AI to continue making steady progress without disruption. This survey provides an entry point for interested researchers and practitioners to learn key aspects of the young and rapidly growing body of research related to XAI. Through the lens of the literature, we review the existing approaches regarding the topic, discuss trends surrounding its sphere, and present major research trajectories.