Widespread adoption of artificial intelligence (AI) technologies is substantially affecting the human condition in ways that are not yet well understood. Negative unintended consequences abound including the perpetuation and exacerbation of societal inequalities and divisions via algorithmic decision making. We present six grand challenges for the scientific community to create AI technologies that are human-centered, that is, ethical, fair, and enhance the human condition. These grand challenges are the result of an international collaboration across academia, industry and government and represent the consensus views of a group of 26 experts in the field of human-centered artificial intelligence (HCAI). In essence, these challenges advocate for a human-centered approach to AI that (1) is centered in human well-being, (2) is designed responsibly, (3) respects privacy, (4) follows human-centered design principles, (5) is subject to appropriate governance and oversight, and (6) interacts with individuals while respecting human’s cognitive capacities. We hope that these challenges and their associated research directions serve as a call for action to conduct research and development in AI that serves as a force multiplier towards more fair, equitable and sustainable societies.

This article aims to investigate the Grand Challenges which arise in the current and emerging landscape of rapid technological evolution towards more intelligent interactive technologies, coupled with increased and widened societal needs, as well as individual and collective expectations that HCI, as a discipline, is called upon to address. A perspective oriented to humane and social values is adopted, formulating the challenges in terms of the impact of emerging intelligent interactive technologies on human life both at the individual and societal levels. Seven Grand Challenges are identified and presented in this article: Human-Technology Symbiosis; Human-Environment Interactions; Ethics, Privacy and Security; Well-being, Health and Eudaimonia; Accessibility and Universal Access; Learning and Creativity; and Social Organization and Democracy. Although not exhaustive, they summarize the views and research priorities of an international interdisciplinary group of experts, reflecting different scientific perspectives, methodological approaches and application domains. Each identified Grand Challenge is analyzed in terms of: concept and problem definition; main research issues involved and state of the art; and associated emerging requirements. BACKGROUND This article presents the results of the collective effort of a group of 32 experts involved in the community of the Human Computer Interaction International (HCII) Conference series. The group’s collaboration started in early 2018 with the collection of opinions from all group members, each asked to independently list and describe five HCI grand challenges. During a one-day meeting held on the 20th July 2018 in the context of the HCI International 2018 Conference in Las Vegas, USA, the identified topics were debated and challenges were formulated in terms of the impact of emerging intelligent interactive technologies on human life both at the individual and societal levels. Further analysis and consolidation led to a set of seven Grand Challenges presented herein. This activity was organized and supported by the HCII Conference series.

With the ever-advancing availability of digitized museum artifacts, the question of how to make the vast collection of exhibits accessible and explorable beyond what museums traditionally offer via their websites and exposed databases has recently gained increased attention. This research work introduces the Invisible Museum: a user-centric platform that allows users to create interactive and immersive virtual 3D/VR exhibitions using a unified collaborative authoring environment. The platform itself was designed following a Human-Centered Design approach, with the active participation of museum curators and end-users. Content representation adheres to domain standards such as International Committee for Documentation of the International Council of Museums (CIDOC-CRM) and the Europeana Data Model and exploits state-of-the-art deep learning technologies to assist the curators by generating ontology bindings for textual data. The platform enables the formulation and semantic representation of narratives that guide storytelling experiences and bind the presented artifacts with their socio-historic context. Main contributions are pertinent to the fields of (a) user-designed dynamic virtual exhibitions, (b) personalized suggestions and exhibition tours, (c) visualization in web-based 3D/VR technologies, and (d) immersive navigation and interaction. The Invisible Museum has been evaluated using a combination of different methodologies, ensuring the delivery of a high-quality user experience, leading to valuable lessons learned, which are discussed in the article.

Culture is a field that is currently entering a revolutionary phase, no longer being a privilege for the few, but expanding to new audiences who are urged to not only passively consume cultural heritage content, but actually participate and assimilate it on their own. In this context, museums have already embraced new technologies as part of their exhibitions, many of them featuring augmented or virtual reality artifacts. The presented work proposes the synthesis of augmented, virtual and mixed reality technologies to provide unified X-Reality experiences in realistic virtual museums, engaging visitors in an interactive and seamless fusion of physical and virtual worlds that will feature virtual agents exhibiting naturalistic behavior. Visitors will be able to interact with the virtual agents, as they would with real world counterparts. The envisioned approach is expected to not only provide refined experiences for museum visitors, but also achieve high quality entertainment combined with more effective knowledge acquisition.

Learning from Demonstration (LfD) constitutes one of the most robust methodologies for constructing efficient cognitive robotic systems. Despite the large body of research works already reported, current key technological challenges include those of multi-agent learning and long-term autonomy. Towards this direction, a novel cognitive architecture for multi-agent LfD robotic learning is introduced in this paper, targeting to enable the reliable deployment of open, scalable and expandable robotic systems in large-scale and complex environments. In particular, the designed architecture capitalizes on the recent advances in the Artificial Intelligence (AI) (and especially the Deep Learning (DL)) field, by establishing a Federated Learning (FL)-based framework for incarnating a multi-human multi-robot collaborative learning environment. The fundamental conceptualization relies on employing multiple AI-empowered cognitive processes (implementing various robotic tasks) that operate at the edge nodes of a network of robotic platforms, while global AI models (underpinning the aforementioned robotic tasks) are collectively created and shared among the network, by elegantly combining information from a large number of human-robot interaction instances. Regarding pivotal novelties, the designed cognitive architecture a) introduces a new FL-based formalism that extends the conventional LfD learning paradigm to support large-scale multi-agent operational settings, b) elaborates previous FL-based self-learning robotic schemes so as to incorporate the human in the learning loop and c) consolidates the fundamental principles of FL with additional sophisticated AI-enabled learning methodologies for modelling the multi-level inter-dependencies among the robotic tasks. The applicability of the proposed framework is explained using an example of a real-world industrial case study (subject to ongoing research activities) for agile production-based Critical Raw Materials (CRM) recovery.