打造可理解智能的挑战
The Challenge of Crafting Intelligible Intelligence
摘要：
由于人工智能（AI）软件使用深度前瞻搜索和大型神经网络的随机优化等技术来适应庞大的数据集，因此通常会导致人们难以理解的复杂行为。然而，组织正在许多关键任务环境中部署AI算法。为了信任它们的行为，我们必须让人工智能变得可理解，要么使用固有的可解释模型，要么开发新的方法来解释和控制极其复杂的决策，使用局部近似、词汇对齐和交互式解释。本文认为，可理解性是必不可少的，调查最近的工作建设这样的系统，并强调了研究的关键方向。
英文摘要：
Since Artificial Intelligence (AI) software uses techniques like deep lookahead search and stochastic optimization of huge neural networks to fit mammoth datasets, it often results in complex behavior that is difficult for people to understand. Yet organizations are deploying AI algorithms in many mission-critical settings. To trust their behavior, we must make AI intelligible, either by using inherently interpretable models or by developing new methods for explaining and controlling otherwise overwhelmingly complex decisions using local approximation, vocabulary alignment, and interactive explanation. This paper argues that intelligibility is essential, surveys recent work on building such systems, and highlights key directions for research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
