Accountable system design architecture for embodied AI: a focus on physical human support robots

Takeda, Mizuki; Hirata, Yasuhisa; Weng, Yueh Hsuan; Katayama, Takahiro; Mizuta, Yasuhide; Koujina, Atsushi

doi:10.1080/01691864.2019.1689168

Cited by 14 publications

(11 citation statements)

References 21 publications

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“…Based on the comments, we know that the users feel that the beginning and the end of leaning is similar for verbal guidance. This tendency can be confirmed on the SUS results, especially for the patterns that adopt both guidance systems (patterns [10][11][12][13][14][15]. It is believed that this is because the leaning motion does not require a significant amount of time.…”

Section: Validation Experiments Of Verbal Guidance For the System Withsupporting

confidence: 54%

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Verbal guidance for sit-to-stand support system

et al. 2020

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Artificial intelligence (AI)-based robots have become popular in various fields, thereby increasing the demand for care robots. Such care robots recognize or estimate factors such as human states and then perform actions depending on the estimation results. If the humans cooperating with robots do not understand robot functions well, the task will not be performed appropriately. Additionally, it is possible that the estimation may be incorrect. Robots may make mistakes, and there is a risk that these mistakes may not be understood by humans. In such cases, robots are not useful, and this may hinder the cooperation between humans and robots. There are several ways to deal with this issue including improving estimation and information representation. We can know what is important to design care robots by analyzing the influence of factors including accurate estimation and contents and timing of information representation. We developed a robot for analysis that can support a human during standing, by raising its armrest. The developed robot raises its armrest when the user leans on it. In the nursing-care field, verbal communication is important. Hence, we also adopt a verbal guidance for information representation. Verbal guidance to encourage a user to stand up and countdown timer representations are adopted. Initially, an experiment is conducted to validate the usability of the system that acts according to considerably accurate estimations. The experimental results show that system without verbal guidance causes anxiety to humans and the system becomes not useful, even if the human state is detected accurately. Furthermore, we determine the appropriate content and timing of verbal guidance based on the results. Subsequently, we conduct an experiment to confirm that the proposed method is applicable under imperfect estimations.

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Section: Validation Experiments Of Verbal Guidance For the System Withsupporting

confidence: 54%

“…Additionally, some researchers have studied systems that confirm human commands [12]. The transparency of the system is important, especially for human-robot interactions [13][14][15]. These information representation methods are often used in considerably accurate systems.…”

Section: Introductionmentioning

confidence: 99%

Verbal guidance for sit-to-stand support system

et al. 2020

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“…It follows from the papers’ degree and level of application that six papers report on actual measures taken to address responsible AI innovation at the level of specific AI-based systems in LTC ( Anderson & Anderson, 2011 ; Armbrust et al, 2011 ; Chaaraoui et al, 2014 ; Körtner, 2016 ; Takeda et al, 2019 ; Yang et al, 2018 ), four of which discuss approaches for the preservation of older adults’ privacy.…”

Section: Resultsmentioning

confidence: 99%

“…For instance, Mahoney et al (2007 , p. 224) suggest to “avoid language that implies the technology does more than it actually does.” In addition, three papers provide suggestions regarding informing users about the purpose of AI technologies and their use of data. These include the provision of up-to-date printed information, and building feedback loops into the systems’ interfaces to help users understand how (their) data are used to predict health care needs ( Körtner, 2016 ; Takeda et al, 2019 ; Wang et al, 2019 ). Five papers discuss that the variety and dynamics of users’ abilities to use, understand, or even consent to using the system must be accounted for in the design and/or implementation of AI technologies ( Matthias, 2015 ; Takeda et al, 2019 ; Tan & Taeihagh, 2021 ; Thorstensen, 2018 ; Wang et al, 2019 ).…”

Section: Resultsmentioning

confidence: 99%

Toward Responsible Artificial Intelligence in Long-Term Care: A Scoping Review on Practical Approaches

et al. 2021

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Background and Objectives Artificial intelligence (AI) is widely positioned to become a key element of intelligent technologies used in the long-term care (LTC) for older adults. The increasing relevance and adoption of AI has encouraged debate over the societal and ethical implications of introducing and scaling AI. This scoping review investigates how the design and implementation of AI technologies in LTC is addressed responsibly: so called responsible innovation (RI). Research Design and Methods We conducted a systematic literature search in five electronic databases using concepts related to LTC, AI and RI. We then performed a descriptive and thematic analysis to map the key concepts, types of evidence and gaps in the literature. Results After reviewing 3,339 papers, 25 papers were identified that met our inclusion criteria. From this literature, we extracted three overarching themes: user-oriented AI innovation; framing AI as a solution to RI issues; and context-sensitivity. Our results provide an overview of measures taken and recommendations provided to address responsible AI innovation in LTC. Discussion and Implications The review underlines the importance of the context of use when addressing responsible AI innovation in LTC. However, limited empirical evidence actually details how responsible AI innovation is addressed in context. Therefore, we recommend expanding empirical studies on RI at the level of specific AI technologies and their local contexts of use. Also, we call for more specific frameworks for responsible AI innovation in LTC to flexibly guide researchers and innovators. Future frameworks should clearly distinguish between RI processes and outcomes.

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“…CoG of Each Link interfaces to ensure accountability and transparency in human-robot interaction [31].…”

Section: Cog Of Whole Bodymentioning

confidence: 99%

Design-Centered HRI Governance for Healthcare Robots

Weng

Hirata

2022

Journal of Healthcare Engineering

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Recent developments have shown that not only are AI and robotics growing more sophisticated, but also these fields are evolving together. The applications that emerge from this trend will break current limitations and ensure that robotic decision making and functionality are more autonomous, connected, and interactive in a way which will support people in their daily lives. However, in areas such as healthcare robotics, legal and ethical concerns will arise as increasingly advanced intelligence functions are incorporated into robotic systems. Using a case study, this paper proposes a unique design-centered approach which tackles the issue of data protection and privacy risk in human-robot interaction.

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Accountable system design architecture for embodied AI: a focus on physical human support robots

Cited by 14 publications

References 21 publications

Verbal guidance for sit-to-stand support system

Verbal guidance for sit-to-stand support system

Toward Responsible Artificial Intelligence in Long-Term Care: A Scoping Review on Practical Approaches

Design-Centered HRI Governance for Healthcare Robots

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