AI Uncertainty Quantification (UQ) has the potential to improve human decision-making beyond AI predictions alone by providing additional probabilistic information to users. The majority of past research on AI and human decision-making has concentrated on model explainability and interpretability, with little focus on understanding the potential impact of UQ on human decision-making. We evaluated the impact on human decision-making for instance-level UQ, calibrated using a strict scoring rule, in two online behavioral experiments. In the first experiment, our results showed that UQ was beneficial for decision-making performance compared to only AI predictions. In the second experiment, we found UQ had generalizable benefits for decision-making across a variety of representations for probabilistic information. These results indicate that implementing high quality, instance-level UQ for AI may improve decision-making with real systems compared to AI predictions alone.

X-ray vision, a technique that allows users to see through walls and other obstacles, is a popular technique for Augmented Reality (AR) and Mixed Reality (MR). In this paper, we demonstrate a dynamic X-ray vision window that is rendered in real-time based on the user’s current position and changes with movement in the physical environment. Moreover, the location and transparency of the window are also dynamically rendered based on the user’s eye gaze. We build this X-ray vision window for a current state-of-the-art MR Head-Mounted Device (HMD) – HoloLens 2 [5] by integrating several different features: scene understanding, eye tracking, and clipping primitive.

Explainable AI provides insights to users into the why for model predictions, offering potential for users to better understand and trust a model, and to recognize and correct AI predictions that are incorrect. Prior research on human and explainable AI interactions has focused on measures such as interpretability, trust, and usability of the explanation. There are mixed findings whether explainable AI can improve actual human decision-making and the ability to identify the problems with the underlying model. Using real datasets, we compare objective human decision accuracy without AI (control), with an AI prediction (no explanation), and AI prediction with explanation. We find providing any kind of AI prediction tends to improve user decision accuracy, but no conclusive evidence that explainable AI has a meaningful impact. Moreover, we observed the strongest predictor for human decision accuracy was AI accuracy and that users were somewhat able to detect when the AI was correct vs. incorrect, but this was not significantly affected by including an explanation. Our results indicate that, at least in some situations, the why information provided in explainable AI may not enhance user decision-making, and further research may be needed to understand how to integrate explainable AI into real systems.

We introduce attacklets, a novel approach to model the high dimensional interactions in cyberattacks. Attacklets are implemented using a real-world dataset of cyberattacks from the Verizon Data Breach Investigation Report. Whereas the commonly used attack graphs model the action sequences of attackers for specific exploits, attacklets model general attributes and states of each attack separately. Attacklets may inform the number and types of attributes across a wide range of cyberattacks. These structural properties can then be used in machine learning models to classify and predict future cyberattacks.