Given the recent successes of Deep Learning in AI there has been increased interest in the role and need for explanations in machine learned theories. A distinct notion in this context is that of Michie’s definition of ultra-strong machine learning (USML). USML is demonstrated by a measurable increase in human performance of a task following provision to the human of a symbolic machine learned theory for task performance. A recent paper demonstrates the beneficial effect of a machine learned logic theory for a classification task, yet no existing work to our knowledge has examined the potential harmfulness of machine’s involvement for human comprehension during learning. This paper investigates the explanatory effects of a machine learned theory in the context of simple two person games and proposes a framework for identifying the harmfulness of machine explanations based on the Cognitive Science literature. The approach involves a cognitive window consisting of two quantifiable bounds and it is supported by empirical evidence collected from human trials. Our quantitative and qualitative results indicate that human learning aided by a symbolic machine learned theory which satisfies a cognitive window has achieved significantly higher performance than human self learning. Results also demonstrate that human learning aided by a symbolic machine learned theory that fails to satisfy this window leads to significantly worse performance than unaided human learning.

鉴于AI深度学习的最新成功，人们对机器学习理论中的角色和解释的兴趣日益浓厚。在这种情况下，一个独特的概念是Michie对超强机器学习（USML）的定义。在向人类提供了用于任务执行的象征性机器学习理论之后，USML的体现是任务的人类绩效有了可衡量的提高。最近的一篇论文证明了机器学习逻辑理论对分类任务的有益作用，但我们所掌握的知识还没有研究检查机器参与对学习过程中人类理解的潜在危害。本文研究了简单的两人游戏背景下机器学习理论的解释效果，并基于认知科学文献，提出了一种识别机器解释有害性的框架。该方法涉及一个由两个可量化范围组成的认知窗口，并得到了来自人体试验的经验证据的支持。我们的定量和定性结果表明，人类学习借助象征性机器学习理论来满足认知窗口已取得比人类自我学习明显更高的性能。结果还证明，在不满足此窗口要求的情况下，借助符号机器学习理论进行的人类学习导致的性能比无人学习的性能明显下降。