464 research outputs found
Robot Mindreading and the Problem of Trust
This paper raises three questions regarding the attribution of beliefs, desires, and intentions to robots. The first one is whether humans in fact engage in robot mindreading. If they do, this raises a second question: does robot mindreading foster trust towards robots? Both of these questions are empirical, and I show that the available evidence is insufficient to answer them. Now, if we assume that the answer to both questions is affirmative, a third and more important question arises: should developers and engineers promote robot mindreading in view of their stated goal of enhancing transparency? My worry here is that by attempting to make robots more mind-readable, they are abandoning the project of understanding automatic decision processes. Features that enhance mind-readability are prone to make the factors that determine automatic decisions even more opaque than they already are. And current strategies to eliminate opacity do not enhance mind-readability. The last part of the paper discusses different ways to analyze this apparent trade-off and suggests that a possible solution must adopt tolerable degrees of opacity that depend on pragmatic factors connected to the level of trust required for the intended uses of the robot
Explain yourself! Effects of Explanations in Human-Robot Interaction
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
Utilising Explanations to Mitigate Robot Conversational Failures
This paper presents an overview of robot failure detection work from HRI and
adjacent fields using failures as an opportunity to examine robot explanation
behaviours. As humanoid robots remain experimental tools in the early 2020s,
interactions with robots are situated overwhelmingly in controlled
environments, typically studying various interactional phenomena. Such
interactions suffer from real-world and large-scale experimentation and tend to
ignore the 'imperfectness' of the everyday user. Robot explanations can be used
to approach and mitigate failures, by expressing robot legibility and
incapability, and within the perspective of common-ground. In this paper, I
discuss how failures present opportunities for explanations in interactive
conversational robots and what the potentials are for the intersection of HRI
and explainability research
Experiential AI: A transdisciplinary framework for legibility and agency in AI
Experiential AI is presented as a research agenda in which scientists and
artists come together to investigate the entanglements between humans and
machines, and an approach to human-machine learning and development where
knowledge is created through the transformation of experience. The paper
discusses advances and limitations in the field of explainable AI; the
contribution the arts can offer to address those limitations; and methods to
bring creative practice together with emerging technology to create rich
experiences that shed light on novel socio-technical systems, changing the way
that publics, scientists and practitioners think about AI.Comment: 10 pages, 3 appendice
- …