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.

With the emergent focus on human-centered artificial intelligence (HCAI), research is required to understand the humanistic aspects of AI design, identify the mechanisms through which user concerns may be alleviated, thereby positively influencing AI adoption. To fill this void, we introduce “Situation Awareness” (SA) as a conceptual framework for considering human-AI interaction (HAII). We argue that SA is an appropriate and valuable theoretical lens through which to decompose and view HAII as hierarchical layers that allow for closer inquiry and discovery. Furthermore, we illustrate why the SA perspective is particularly relevant to the current need to understand HCAI by identifying three tensions inherent in AI design and explaining how an SA-oriented approach may help alleviate these tensions. We posit that users’ enactment of SA will mitigate some negative impacts of AI systems on user experience, improve human agency during AI system use, and promote more efficient and effective in-situ decision-making.

Virtual reality (VR) has served the entertainment industry all the way to world-leading museums in delivering engaging experiences through multisensory virtual environments (VEs). Today, the rise of the Metaverse fuels a growing interest in leveraging this technology, bringing along an emerging need to better understand the way different dimensions of VEs, namely social and interactive, impact overall user experience (UX). This between-subject exploratory field study investigates differences in the perceived and lived experience of 28 participants engaging, either individually or in dyads, in a VR experience comprising different levels of interactivity, i.e., passive or active. A mixed methods approach combining conventional UX measures, i.e., psychometric surveys and user interviews, as well as psychophysiological measures, i.e., wearable bio- and motion sensors, allowed for a comprehensive assessment of users' immersive and affective experiences. Results pertaining to the social dimension of the experience reveal that shared VR elicits significantly more positive affect, whereas presence, immersion, flow, and state anxiety are unaffected by the copresence of a real-world partner. Results pertaining to the interactive dimension of the experience suggest that the interactivity afforded by the VE moderates the effect of copresence on users' adaptive immersion and arousal. These results support that VR can be shared with a real-world partner not only without hindering the immersive experience, but also by enhancing positive affect. Hence, in addition to offering methodological directions for future VR field research, this study provides interesting practical insights into guiding VR developers toward optimal multiuser virtual environments.

As the world adapts to COVID-19, the transport behaviour of commuters has been greatly modified. Governments and transit authorities will need strong, well-received mitigation measures and education campaigns to maintain the historically upward trend of sustainable mass transit usage following this pandemic. This study, from a survey of 1968 Canadians in early May 2020, reveals that, following the end of stay-at-home orders, commuters intend to use their cars more and mass transit less. Driving these behavioural changes are commuters’ perceptions that mass transit use will negatively impact their health safety, peace of mind, and travel experience. The results also show that certain mitigation measures, such as more frequent cleaning and mandatory hand washing, are likely to reduce this decline, whereas e-monitoring and the use of health certificates will be detrimental to mass transit ridership through user perception. These results can help lessen the environmental impact of the public returning to work by encouraging their continued use of more environmentally friendly modes of transportation.

Drawing from the tension between a company’s desire for customer information to tailor experiences and a consumer’s need for privacy, this study aims to test the effect of two information disclosure nudges on users’ information disclosure behaviors. Whereas previous literature on user-chatbot interaction focused on encouraging and increasing users’ disclosures, this study introduces measures that make users conscious of their disclosure behaviors to low and high-sensitivity questions asked by chatbots. A within-subjects laboratory experiment entailed 19 participants interacting with chatbots, responding to pre-tested questions of varying sensitivity while being presented with different information disclosure nudges. The results suggest that question sensitivity negatively impacts users’ information disclosures to chatbots. Moreover, this study suggests that adding a sensitivity signal—presenting the level of sensitivity of the question asked by the chatbot—influences users’ information disclosure behaviors. Finally, the theoretical contributions and managerial implications of the results are discussed.

Advice-giving systems such as decision support systems and recommender systems (RS) utilize algorithms to provide users with decision support by generating ‘advice’ ranging from tailored alerts for situational exception events to product recommendations based on preferences. Related extant research of user perceptions and behaviors has predominantly taken a system-level view, whereas limited attention has been given to the impact of message design on recommendation acceptance and system use intentions. Here, a comprehensive model was developed and tested to explore the presentation choices (i.e., recommendation message characteristics) that influenced users’ confidence in—and likely acceptance of—recommendations generated by the RS. Our findings indicate that the problem and solution-related information specificity of the recommendation increase both user intention and the actual acceptance of recommendations while decreasing the decision-making time; a shorter decision-making time was also observed when the recommendation was structured in a problem-to-solution sequence. Finally, information specificity was correlated with information sufficiency and transparency, confirming prior research with support for the links between user beliefs, user attitudes, and behavioral intentions. Implications for theory and practice are also discussed.