Transparency as an Ethical Safeguard

Spagnolli, Anna; Frank, Lily; Haselager, Pim; Kirsh, David

doi:10.1007/978-3-319-91593-7_1

Cited by 7 publications

(7 citation statements)

References 4 publications

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“…Transparency can be defined as the extent to which the system discloses the processes or parameters that relate to its functioning ( Spagnolli et al, 2016 ). Transparency can also be considered as the property that makes it possible to discover how and why the system made a particular decision or acted the way it did ( Chatila et al, 2017 ), taking into account its environment ( Lakhmani et al, 2016 ).…”

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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“…In [49], the authors identify nonverbal cues that signal untrustworthy behavior and also demonstrate the human mind's readiness to interpret those cues to assess the trustworthiness of a social robot. Transparency could be considered as an enabling mechanism for successfully fulfilling some ethical principles [50]. The interplay between transparency and ethical principles is of primordial importance in interactive machine learning frameworks, since the machines continuously collect implicit data from users.…”

Section: Emotion and Transparency In Roboticsmentioning

confidence: 99%

Towards Transparent Robot Learning Through TDRL-Based Emotional Expressions

Broekens

Chétouani

2021

IEEE Trans. Affective Comput.

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Robots and virtual agents need to adapt existing and learn novel behavior to function autonomously in our society. Robot learning is often in interaction with or in the vicinity of humans. As a result the learning process needs to be transparent to humans. Reinforcement Learning (RL) has been used successfully for robot task learning. However, this learning process is often not transparent to the users. This results in a lack of understanding of what the robot is trying to do and why. The lack of transparency will directly impact robot learning. The expression of emotion is used by humans and other animals to signal information about the internal state of the individual in a language-independent, and even species-independent way, also during learning and exploration. In this article we argue that simulation and subsequent expression of emotion should be used to make the learning process of robots more transparent. We propose that the TDRL Theory of Emotion gives sufficient structure on how to develop such an emotionally expressive learning robot. Finally, we argue that next to such a generic model of RL-based emotion simulation we need personalized emotion interpretation for robots to better cope with individual expressive differences of users.

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“…Other authors have previously written about explainability in the form of position papers and reviews [24,26,38,53,68,71].…”

Section: Similar Reviewsmentioning

confidence: 99%

Explainable Embodied Agents Through Social Cues

Wallkotter

Tulli

Castellano

et al. 2021

J. Hum.-Robot Interact.

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The issue of how to make embodied agents explainable has experienced a surge of interest over the past 3 years, and there are many terms that refer to this concept, such as transparency and legibility. One reason for this high variance in terminology is the unique array of social cues that embodied agents can access in contrast to that accessed by non-embodied agents. Another reason is that different authors use these terms in different ways. Hence, we review the existing literature on explainability and organize it by (1) providing an overview of existing definitions, (2) showing how explainability is implemented and how it exploits different social cues, and (3) showing how the impact of explainability is measured. Additionally, we present a list of open questions and challenges that highlight areas that require further investigation by the community. This provides the interested reader with an overview of the current state of the art.

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Transparency as an Ethical Safeguard

Cited by 7 publications

References 4 publications

IEEE P7001: A Proposed Standard on Transparency

IEEE P7001: A Proposed Standard on Transparency

Towards Transparent Robot Learning Through TDRL-Based Emotional Expressions

Explainable Embodied Agents Through Social Cues

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