Accountable system design architecture for embodied AI: a focus on physical human support robots
Accountable system design architecture for embodied AI: a focus on physical human support robots
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DOI：
10.1080/01691864.2019.1689168
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发表时间：
2019-11-13
影响因子：
2
通讯作者：
Koujina, Atsushi
中科院分区：
文献类型：
--
作者：
Takeda, Mizuki;Hirata, Yasuhisa;Koujina, Atsushi
尽管基于机器人的老年人支持系统的开发越来越受欢迎，但人类很难理解自主机器人的行动、计划和行为及其背后的原因，特别是当机器人包含学习算法时。被称为人工智能的基于学习的自主系统被视为本质上不值得信赖的“黑匣子”，因为机器学习或深度学习算法对人类来说很难理解。然而，应该信任与人类密切合作的辅助机器人等机器人系统。因此，系统应实现对所有利益相关者的问责。然而，该领域的大多数研究都集中在特定的系统和情况上，没有通用的设计架构。在这项研究中，我们提出了一种新的设计方法，专注于问责制和透明度，用于基于学习的机器人系统。描述整个系统是必要的第一步，并且根据几个原则为每个涉众转录所描述的系统对于实现问责制是有效的。该方法提高了系统的透明度，包括学习算法。站立辅助机器人作为整个系统的一个例子，以阐明系统的哪些部分需要更大的透明度。本研究采用系统建模语言（SysML）对系统进行描述，并将所描述的系统进行信息表示。应该考虑涉众、信息和系统接口之间的关系来表示信息。由于其复杂性，人类很难理解机器人系统中可用的完整信息集。因此，系统应根据具体情况只提供所需的信息。涉众-接口关系也很重要，因为它更有利于专业人员查看与其专业领域相关的信息，而其他人很难理解这些信息。相比之下，界面对于一般用户来说应该是直观的。可视化和声音是传递信息的非常有用的手段，在不同的情况下有其优缺点。这些关系对于实现问责制非常重要。最后，我们展示了一个已开发的支持系统的实现示例。验证了基于所提出的设计体系结构可以设计问责制。
Although the development of robot-based support systems for elderly people has become more popular, it is difficult for humans to understand the actions, plans, and behavior of autonomous robots and the reasons behind them, particularly when the robots include learning algorithms. Learning-based autonomous systems which are called AI are treated as an inherently untrustworthy 'black box,' because machine learning or deep learning algorithms are difficult for humans to understand. Robot systems such as assistive robots, which work closely with humans, however, should be trusted. Systems should therefore achieve accountability for all stakeholders. However, most research in this field has focused on particular systems and situations, and no general design architecture exists. In this study, we propose a new design method, focused on accountability and transparency, for learning-based robot systems. Describing the entire system is a necessary first step, and transcribing the described system for each stakeholder based on several principles is effective for achieving accountability. The method improves transparency for systems, including learning algorithms. A standing assistive robot is used as an example of the entire system to clarify which system parts require greater transparency. This study adopted the Systems Modeling Language (SysML) to describe the system and the described system is used for the information representation. Information should be represented considering the relationships between stakeholders, information, and the system interface. Because of their complexity, it is difficult for humans to understand the complete set of information available in robot systems. Systems should therefore present only the information required, depending on the situation. The stakeholder-interface relationship is also important because it is more beneficial for professionals to view information relevant to their specialized field, which would be difficult for others to understand. By contrast, the interface should be intuitive for general users. Visualization and sound are very useful means of transmitting information, with advantages and disadvantages for different circumstances. These relationships are important for achieving accountability. Finally, we show an example of implementation with a developed support system. It is confirmed that accountable systems can be designed based on the proposed design architecture.