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تسجيل مستخدم جديدProceedings of the AI-HRI Symposium at AAAI-FSS 2020
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The Artificial Intelligence (AI) for Human-Robot Interaction (HRI) Symposium has been a successful venue of discussion and collaboration since 2014. In that time, the related topic of trust in robotics has been rapidly growing, with major research efforts at universities and laboratories across the world. Indeed, many of the past participants in AI-HRI have been or are now involved with research into trust in HRI. While trust has no consensus definition, it is regularly associated with predictability, reliability, inciting confidence, and meeting expectations. Furthermore, it is generally believed that trust is crucial for adoption of both AI and robotics, particularly when transitioning technologies from the lab to industrial, social, and consumer applications. However, how does trust apply to the specific situations we encounter in the AI-HRI sphere? Is the notion of trust in AI the same as that in HRI? We see a growing need for research that lives directly at the intersection of AI and HRI that is serviced by this symposium. Over the course of the two-day meeting, we propose to create a collaborative forum for discussion of current efforts in trust for AI-HRI, with a sub-session focused on the related topic of explainable AI (XAI) for HRI.
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To facilitate the widespread acceptance of AI systems guiding decision-making in real-world applications, it is key that solutions comprise trustworthy, integrated human-AI systems. Not only in safety-critical applications such as autonomous driving
or medicine, but also in dynamic open world systems in industry and government it is crucial for predictive models to be uncertainty-aware and yield trustworthy predictions. Another key requirement for deployment of AI at enterprise scale is to realize the importance of integrating human-centered design into AI systems such that humans are able to use systems effectively, understand results and output, and explain findings to oversight committees. While the focus of this symposium was on AI systems to improve data quality and technical robustness and safety, we welcomed submissions from broadly defined areas also discussing approaches addressing requirements such as explainable models, human trust and ethical aspects of AI.
Proceedings of the AAAI Fall Symposium on Artificial Intelligence in Government and Public Sector, Arlington, Virginia, USA, October 18-20, 2018
This volume contains papers presented at the Ninth International Symposium on Symbolic Computation in Software Science, SCSS 2021. Symbolic Computation is the science of computing with symbolic objects (terms, formulae, programs, representations of
algebraic objects, etc.). Powerful algorithms have been developed during the past decades for the major subareas of symbolic computation: computer algebra and computational logic. These algorithms and methods are successfully applied in various fields, including software science, which covers a broad range of topics about software construction and analysis. Meanwhile, artificial intelligence methods and machine learning algorithms are widely used nowadays in various domains and, in particular, combined with symbolic computation. Several approaches mix artificial intelligence and symbolic methods and tools deployed over large corpora to create what is known as cognitive systems. Cognitive computing focuses on building systems that interact with humans naturally by reasoning, aiming at learning at scale. The purpose of SCSS is to promote research on theoretical and practical aspects of symbolic computation in software science, combined with modern artificial intelligence techniques. These proceedings contain the keynote paper by Bruno Buchberger and ten contributed papers. Besides, the conference program included three invited talks, nine short and work-in-progress papers, and a special session on computer algebra and computational logic. Due to the COVID-19 pandemic, the symposium was held completely online. It was organized by the Research Institute for Symbolic Computation (RISC) of the Johannes Kepler University Linz on September 8--10, 2021.
Artificial intelligence (AI) and robotic coaches promise the improved engagement of patients on rehabilitation exercises through social interaction. While previous work explored the potential of automatically monitoring exercises for AI and robotic c
oaches, the deployment of these systems remains a challenge. Previous work described the lack of involving stakeholders to design such functionalities as one of the major causes. In this paper, we present our efforts on eliciting the detailed design specifications on how AI and robotic coaches could interact with and guide patients exercises in an effective and acceptable way with four therapists and five post-stroke survivors. Through iterative questionnaires and interviews, we found that both post-stroke survivors and therapists appreciated the potential benefits of AI and robotic coaches to achieve more systematic management and improve their self-efficacy and motivation on rehabilitation therapy. In addition, our evaluation sheds light on several practical concerns (e.g. a possible difficulty with the interaction for people with cognitive impairment, system failures, etc.). We discuss the value of early involvement of stakeholders and interactive techniques that complement system failures, but also support a personalized therapy session for the better deployment of AI and robotic exercise coaches.
The 2nd edition of the Montreal AI Ethics Institutes The State of AI Ethics captures the most relevant developments in the field of AI Ethics since July 2020. This report aims to help anyone, from machine learning experts to human rights activists an
d policymakers, quickly digest and understand the ever-changing developments in the field. Through research and article summaries, as well as expert commentary, this report distills the research and reporting surrounding various domains related to the ethics of AI, including: AI and society, bias and algorithmic justice, disinformation, humans and AI, labor impacts, privacy, risk, and future of AI ethics. In addition, The State of AI Ethics includes exclusive content written by world-class AI Ethics experts from universities, research institutes, consulting firms, and governments. These experts include: Danit Gal (Tech Advisor, United Nations), Amba Kak (Director of Global Policy and Programs, NYUs AI Now Institute), Rumman Chowdhury (Global Lead for Responsible AI, Accenture), Brent Barron (Director of Strategic Projects and Knowledge Management, CIFAR), Adam Murray (U.S. Diplomat working on tech policy, Chair of the OECD Network on AI), Thomas Kochan (Professor, MIT Sloan School of Management), and Katya Klinova (AI and Economy Program Lead, Partnership on AI). This report should be used not only as a point of reference and insight on the latest thinking in the field of AI Ethics, but should also be used as a tool for introspection as we aim to foster a more nuanced conversation regarding the impacts of AI on the world.
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