解释“解释”
Explaining Explaining
AI总结：
针对机器学习黑箱无法提供真实解释的问题，本文提出一种以知识基础设施为主、机器学习为辅的混合认知智能体方法，并通过模拟机器人协作搜索任务展示其解释能力。
摘要：
解释对于人们信任高风险人工智能系统至关重要。然而，基于机器学习的系统——几乎涵盖了当前所有的人工智能——无法进行解释，因为它们通常是黑箱。可解释人工智能（XAI）运动通过重新定义“解释”来回避这一问题。以人为中心的可解释人工智能（HCXAI）运动识别了用户面向解释的需求，但由于其对机器学习的坚持而无法满足这些需求。为了实现关键领域中真实操作人员所需的那种解释，我们必须重新思考人工智能的研究路径。我们描述了一种开发认知智能体的混合方法，该方法使用基于知识的基础设施，并在适用时辅以通过机器学习获得的数据。这些智能体将作为人类的助手，而人类将对人机团队的决策和行动承担最终责任。我们利用一个演示系统的内部面板展示了这类智能体的解释潜力，在该系统中，一组模拟机器人在人类分配的一项搜索任务上进行协作。
英文摘要：
Explanation is key to people having confidence in high-stakes AI systems. However, machine-learning-based systems -- which account for almost all current AI -- can't explain because they are usually black boxes. The explainable AI (XAI) movement hedges this problem by redefining "explanation". The human-centered explainable AI (HCXAI) movement identifies the explanation-oriented needs of users but can't fulfill them because of its commitment to machine learning. In order to achieve the kinds of explanations needed by real people operating in critical domains, we must rethink how to approach AI. We describe a hybrid approach to developing cognitive agents that uses a knowledge-based infrastructure supplemented by data obtained through machine learning when applicable. These agents will serve as assistants to humans who will bear ultimate responsibility for the decisions and actions of the human-robot team. We illustrate the explanatory potential of such agents using the under-the-hood panels of a demonstration system in which a team of simulated robots collaborate on a search task assigned by a human.
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。
