Determine if situated visual alarm displays can support machine fitness assessment (MFA),
facilitating improved hazard recognition and alarm accuracy assessment in the presence of …

Quantitative evaluations of human-machine teams (HMTs) are desperately needed to
ensure technological implementations are helpful rather than harmful to overall system …

Despite the promise of a proactive approach to safety, a lack of resources and tangible
measures have limited its implementation in organizations. We are exploring Joint Activity …

The purpose of this study is to examine the impact of an example-based explainable artificial
intelligence (XAI) interface on trust, understanding, and performance in highly-technical …

As machines increasingly behave more like active cognitive agents than passive tools,
additional heuristics for supporting joint human-machine activity are urgently needed to …

There is a considerable body of research on trust in Artificial Intelligence (AI). Trust has been
viewed almost exclusively as a dyadic construct, where it is a function of various factors …

Artificial Intelligence (AI) is often viewed as the means by which the intelligence community
will cope with the increasing amount of information available to them. Trust is a complex …

Explainable AI must simultaneously help people understand the world, the AI, and when the
AI is misaligned to the world. We propose situated interpretation and data (SID) as a design …

Deep learning (DL) methods have shown great promise in auto-segmentation problems.
However, for head and neck cancer, we show that DL methods fail at the axial edges of the …

Capable computational technology is a necessary but not a sufficient condition for
engineering a high-performing human-machine team (HMT). The interactions between …