论文标题
可解释的人工智能和机器学习：现实扎根的视角
Explainable Artificial Intelligence and Machine Learning: A reality rooted perspective
论文作者
论文摘要
由于技术进步，我们习惯了几乎所有科学领域产生的大数据。但是，对此类数据的分析面临着巨大的挑战。其中之一与人工智能（AI）或机器学习方法的解释性有关。当前，许多此类方法在其工作机制方面是非透明的，因此，这是黑匣子模型，最著名的是深度学习方法。但是，已经意识到，这构成了许多领域的严重问题，包括健康科学和刑事司法和论点，而有利于可解释的AI。在本文中，我们不假定通常可以说明AI的观点，而是我们提供了讨论可以解释的AI。不同之处在于，我们没有提出一厢情愿，而是与物理学以外的科学理论有关的现实属性。
We are used to the availability of big data generated in nearly all fields of science as a consequence of technological progress. However, the analysis of such data possess vast challenges. One of these relates to the explainability of artificial intelligence (AI) or machine learning methods. Currently, many of such methods are non-transparent with respect to their working mechanism and for this reason are called black box models, most notably deep learning methods. However, it has been realized that this constitutes severe problems for a number of fields including the health sciences and criminal justice and arguments have been brought forward in favor of an explainable AI. In this paper, we do not assume the usual perspective presenting explainable AI as it should be, but rather we provide a discussion what explainable AI can be. The difference is that we do not present wishful thinking but reality grounded properties in relation to a scientific theory beyond physics.
