Autonomous Systems, Trust, and Guarantees

TaheriNejad, Nima; Herkersdorf, Andreas; Jantsch, Axel

doi:10.1109/mdat.2020.3024145

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Indeed, at the moment, there is no single definition of transparency in the literature ( Theodorou et al, 2017 ; Larsson and Heintz, 2020 ), as it varies depending on its application domain ( Weller, 2019 ) and its dimensions ( Bertino et al, 2019 ). The notion of transparency is also often interwoven with other related concepts such as fairness ( Olhede and Rodrigues, 2017 ), trustworthiness ( Wortham, 2020 ; Nesset et al, 2021 ), interpretability ( Gilpin et al, 2018 ), accountability ( Koene et al, 2019 ), dependability ( TaheriNejad et al, 2020 ), reliability ( Wright et al, 2020 ), and/or safety ( Burton et al, 2020 ).…”

Section: Related Workmentioning

confidence: 99%

IEEE P7001: A Proposed Standard on Transparency

et al. 2021

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This paper describes IEEE P7001, a new draft standard on transparency of autonomous systems1. In the paper, we outline the development and structure of the draft standard. We present the rationale for transparency as a measurable, testable property. We outline five stakeholder groups: users, the general public and bystanders, safety certification agencies, incident/accident investigators and lawyers/expert witnesses, and explain the thinking behind the normative definitions of “levels” of transparency for each stakeholder group in P7001. The paper illustrates the application of P7001 through worked examples of both specification and assessment of fictional autonomous systems.

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Section: Related Workmentioning

confidence: 99%

IEEE P7001: A Proposed Standard on Transparency

et al. 2021

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Harnessing Approximate Computing for Machine Learning

Shakibhamedan,

Aminifar,

Vassallo

et al. 2024

2024 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)

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Who’s in Charge Here? A Survey on Trustworthy AI in Variable Autonomy Robotic Systems

Methnani,

Chiou,

Dignum

et al. 2024

ACM Comput. Surv.

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This paper surveys the Variable Autonomy (VA) robotics literature that considers two contributory elements to Trustworthy AI: transparency and explainability. These elements should play a crucial role when designing and adopting robotic systems, especially in VA where poor or untimely adjustments of the system’s level of autonomy can lead to errors, control conflicts, user frustration and ultimate disuse of the system. Despite this need, transparency and explainability is, to the best of our knowledge, mostly overlooked in VA robotics literature or is not considered explicitly. In this paper, we aim to present and examine the most recent contributions to the VA literature concerning transparency and explainability. In addition, we propose a way of thinking about VA by breaking these two concepts down based on: the mission of the human-robot team; who the stakeholder is; what needs to be made transparent or explained; why they need it; and how it can be achieved. Last, we provide insights and propose ways to move VA research forward. Our goal with this paper is to raise awareness and inter-community discussions among the Trustworthy AI and the VA robotics communities.

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Autonomous Systems, Trust, and Guarantees

Cited by 3 publications

References 11 publications

IEEE P7001: A Proposed Standard on Transparency

IEEE P7001: A Proposed Standard on Transparency

Harnessing Approximate Computing for Machine Learning

Who’s in Charge Here? A Survey on Trustworthy AI in Variable Autonomy Robotic Systems

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