Explainable Autonomous Robots: A Survey and Perspective

Sakai, Tatsuya; Nagai, Takayuki

doi:10.48550/arxiv.2105.02658

Cited by 3 publications

(2 citation statements)

References 58 publications

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“…The effects and challenges of privacy in AI-based equipment are described in the studies [73]- [75]. Autonomous technologies such as drones [76], selfdriving cars [77], robots [78], and personal assistants [79] acquire a significant amount of data from their users. This data is gathered through surveillance and interrogatory ways [80].…”

Section: Preserving Data Privacy In Autonomous Systemsmentioning

confidence: 99%

Data Privacy Threat Modelling for Autonomous Systems: A Survey From the GDPR's Perspective

Azam

Michala

Ansari

et al. 2023

IEEE Trans. Big Data

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Intelligence-based applications have been increasingly deployed in every field of life including smart homes, smart cities, healthcare services, and autonomous systems where personal data is collected across heterogeneous sources and processed using "black-box" algorithms in opaque centralised servers. As a consequence, preserving the data privacy and security of these applications is of utmost importance. In this respect, a modelling technique for identifying potential data privacy threats and specifying countermeasures to mitigate the related vulnerabilities in such AI-based systems plays a significant role in preserving and securing personal data. Various threat modelling techniques have been proposed such as STRIDE, LINDDUN, and PASTA but none of them is sufficient to model the data privacy threats in autonomous systems. Furthermore, they are not designed to model compliance with data protection legislation like the EU/UK General Data Protection Regulation (GDPR), which is fundamental to protecting data owners' privacy as well as to preventing personal data from potential privacy-related attacks. In this article, we survey the existing threat modelling techniques for data privacy threats in autonomous systems and then analyse such techniques from the viewpoint of GDPR compliance. Following the analysis, We employ STRIDE and LINDDUN in autonomous cars, a specific use-case of autonomous systems, to scrutinise the challenges and gaps of the existing techniques when modelling data privacy threats. Prospective research directions for refining data privacy threats & GDPR-compliance modelling techniques for autonomous systems are also presented.

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Section: Preserving Data Privacy In Autonomous Systemsmentioning

confidence: 99%

Data Privacy Threat Modelling for Autonomous Systems: A Survey From the GDPR's Perspective

Azam

Michala

Ansari

et al. 2023

IEEE Trans. Big Data

View full text Add to dashboard Cite

show abstract

“…The majority of the methods that have been proposed generate explanations that are understandable only for experts, rather then potential users [27]. On the other hand, in the field of autonomous robotics [28,29] and in human-robot interaction [7,30], explaining actions to a user rather than an expert is particularly important. This is especially pronounced in humanrobot cooperative tasks [31].…”

Section: Related Workmentioning

confidence: 99%

Explain yourself! Effects of Explanations in Human-Robot Interaction

Ambsdorf¹,

Munir²,

Wei³

et al. 2022

Preprint

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Recent developments in explainable artificial intelligence promise the potential to transform human-robot interaction: Explanations of robot decisions could affect user perceptions, justify their reliability, and increase trust. However, the effects on human perceptions of robots that explain their decisions have not been studied thoroughly. To analyze the effect of explainable robots, we conduct a study in which two simulated robots play a competitive board game. While one robot explains its moves, the other robot only announces them. Providing explanations for its actions was not sufficient to change the perceived competence, intelligence, likeability or safety ratings of the robot. However, the results show that the robot that explains its moves is perceived as more lively and human-like. This study demonstrates the need for and potential of explainable human-robot interaction and the wider assessment of its effects as a novel research direction.

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A Framework of Explanation Generation toward Reliable Autonomous Robots

Sakai

Miyazawa

Horii

et al. 2021

Preprint

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To realize autonomous collaborative robots, it is important to increase the trust that users have in them. Toward this goal, this paper proposes an algorithm which endows an autonomous agent with the ability to explain the transition from the current state to the target state in a Markov decision process (MDP). According to cognitive science, to generate an explanation that is acceptable to humans, it is important to present the minimum information necessary to sufficiently understand an event. To meet this requirement, this study proposes a framework for identifying important elements in the decision-making process using a prediction model for the world and generating explanations based on these elements. To verify the ability of the proposed method to generate explanations, we conducted an experiment using a grid environment. It was inferred from the result of a simulation experiment that the explanation generated using the proposed method was composed of the minimum elements important for understanding the transition from the current state to the target state. Furthermore, subject experiments showed that the generated explanation was a good summary of the process of state transition, and that a high evaluation was obtained for the explanation of the reason for an action.

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Explainable Autonomous Robots: A Survey and Perspective

Cited by 3 publications

References 58 publications

Data Privacy Threat Modelling for Autonomous Systems: A Survey From the GDPR's Perspective

Data Privacy Threat Modelling for Autonomous Systems: A Survey From the GDPR's Perspective

Explain yourself! Effects of Explanations in Human-Robot Interaction

A Framework of Explanation Generation toward Reliable Autonomous Robots

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