*apologies for cross-postings*
HRI 2018 Workshop
Explainable Robotic Systems
Held on March 5 in conjunction with HRI 2018 conference, Chicago, IL, USA.
The call for Autonomous Intelligent Systems (AIS) to be transparent has recently become loud and clear and currently is a pressing funding and research agenda. Some forms of transparency, such as traceability and verification, are particularly important for software and hardware engineers; other forms, such as explainability or intelligibility, are particularly important for ordinary people. As artificial agents, and especially socially interactive robots, enter human society, the demands for these agents to be transparent and explainable grow rapidly. When a system is able, for example, to explain how it made classifications or arrived at a judgment, users are better able to judge the accuracy and adequacy of the systems and have better calibrated trust in them.
More and more AI systems process vast amounts of information and make classifications or recommendations that humans use for financial, employment, medical, military, and political decisions. More precariously yet, autonomous social robots, by definition, make decisions reaching beyond direct commands and perform actions with social and moral significance for humans. The demand for these agents to become transparent and explainable is particularly urgent. However, to make robots explainable, we need to understand how people interpret the behavior of such systems and what expectations they have of them.
Aim of the workshop
In this workshop, we will address the topics of transparency and explainability, for robots in particular, from both the cognitive-science perspective and the computer-science and robotics perspective. Cognitive science elucidates how people interpret robot behavior; computer science and robotics elucidate how the computational mechanisms underlying robot behavior can be structured and communicated so as to be human-interpretable. The implementation and use of explainable robotic systems may prevent the potentially frightening confusion over why a robot is behaving the way it is. Moreover, explainable robot systems may allow people to better calibrate their expectations of the robot’s capabilities and be less prone to treating robots as almost-humans.
The aim of this full-day workshop is to provide a forum to share and learn about recent research on requirements for artificial agents’ explanations as well as the implementation of transparent, predictable and explainable robotic systems. Extended time for discussions will highlight and document promising approaches and encourage further work. A large part of this effort is to bring together a community of researchers, strengthen existing connections, and build new ones.
The morning session will cover invited talks from Rachid Alami, Joanna Bryson, Bradley Hayes, and Alessandra Sciutti, and short presentations in themed discussion sessions around the key topics that are raised by accepted paper submissions.
The afternoon will be devoted to break-out sessions to discuss the next steps in research and development of explainable and transparent robotic systems. Groups will be composed of representatives of different disciplines in order to work on integrating the multiple necessary perspectives in this endeavor. To boost the discussions, we will ask presenters to prepare questions or raise pressing issues that provide starting points for the discussion groups.
Call for papers
In this workshop, we want to bring together researchers and practitioners from a wide range of different disciplines who are interested in explainable robot and AI systems. We welcome multidisciplinary contributions that intersect with robot systems (e.g. human-human interaction, HCI, HRI, human factors, engineering, computer sciences, cognitive science, interactive design, sociology, anthropology, psychology, philosophy).
We welcome prospective participants to submit extended abstracts (max. 2 pages) covering any topic that could potentially contribute to the discussion of people’s interpretation of robot actions as well as the implementation of transparent, predictable and explainable behaviors in robotic systems. All papers should be submitted in PDF format using the HRI LBR template, and should be sent to [hidden email] <mailto:[hidden email]> . All submitted papers within the scope of the workshop will be peer-reviewed. Papers will be selected based on their originality, relevance, contributions, technical clarity, and presentation. Accepted papers will require that at least one author registers for and attends the workshop.
After the conference, and with permission of the authors, we will provide online access to the workshop proceedings on this website. In addition, we have submitted a special issue proposal to the ACM Transactions on Human-Robot Interaction (formerly the Journal of HRI), giving accepted authors the opportunity to disseminate full-versions of their work and other ideas developed during the workshop.
We encourage researchers to attend the workshop even without a paper submission. Our goal is to maximize community engagement and the uptake of concepts regarding explainable robotic systems within the field of HRI.
Submission deadline: January 12, 2018
Acceptance notification: January 31, 2018 (or before the early bird registration)
Camera-ready deadline: February 15, 2018
Maartje De Graaf - Brown University, USA
Bertram Malle - Brown University, USA
Anca Dragan – UC Berkely, USA
Tom Ziemke - University of Skövde and Linköping University, Swede
For more information: <https://urldefense.proofpoint.com/v2/url?u=https-3A__explainableroboticsystems.wordpress.com&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=zQo80R3UzdqP62KQydAXz2PvmlPuWHlsecK1d4KQ3F4&s=PGQpcshIFcejH7rLjr90Wgk-QMZX8BX2rltKyzuUoeE&e=> https://urldefense.proofpoint.com/v2/url?u=https-3A__explainableroboticsystems.wordpress.com&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=zQo80R3UzdqP62KQydAXz2PvmlPuWHlsecK1d4KQ3F4&s=PGQpcshIFcejH7rLjr90Wgk-QMZX8BX2rltKyzuUoeE&e=
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