This study, introduces and evaluates different countermeasures using real-time eye-tracking data. The countermeasures detect when driver gaze deviates from the road for longer than a predetermined threshold and then redirect the driver's attention back to the road. The countermeasures include bimodal and trimodal alerts using combinations of auditory, tactile, and visual modalities. These countermeasures showcase the utility of adopting eye-tracking technologies in the context of driver monitoring and advanced driver's assistance systems. They enhance safety as a safeguard for the increased use of devices such as in-vehicle infotainment systems. Results show that countermeasures effectively redirect drivers’ attention to the road, with higher on-road gaze time. Additionally, bimodal alerts that include the visual modality are less effective at redirecting participants’ gaze on-road and result in poorer driving performance.

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.