Establishing and evaluating trustworthy AI: overview and research challenges.

Artificial intelligence (AI) technologies (re-)shape modern life, driving innovation in a wide range of sectors. However, some AI systems have yielded unexpected or undesirable outcomes or have been used in questionable manners. As a result, there has been a surge in public and academic discussions about aspects that AI systems must fulfill to be considered trustworthy.

Counting on AR: EEG responses to incongruent information with real-world context.

Augmented Reality (AR) technologies enhance the real world by integrating contextual digital information about physical entities. However, inconsistencies between physical reality and digital augmentations, which may arise from errors in the visualized information or the user's mental context, can considerably impact user experience. This work characterizes the brain dynamics associated with processing incongruent information within an AR environment.

Continual learning in the presence of repetition.

Continual learning (CL) provides a framework for training models in ever-evolving environments. Although re-occurrence of previously seen objects or tasks is common in real-world problems, the concept of repetition in the data stream is not often considered in standard benchmarks for CL. Unlike with the rehearsal mechanism in buffer-based strategies, where sample repetition is controlled by the strategy, repetition in the data stream naturally stems from the environment.

Importance estimate of features via analysis of their weight and gradient profile.

Understanding what is important and redundant within data can improve the modelling process of neural networks by reducing unnecessary model complexity, training time and memory storage. This information is however not always priorly available nor trivial to obtain from neural networks. There are existing feature selection methods which utilise the internal working of a neural network for selection, however further analysis and interpretation of the input features' significance is often limiting.

Multimodal decoding of error processing in a virtual reality flight simulation.

Technological advances in head-mounted displays (HMDs) facilitate the acquisition of physiological data of the user, such as gaze, pupil size, or heart rate. Still, interactions with such systems can be prone to errors, including unintended behavior or unexpected changes in the presented virtual environments. In this study, we investigated if multimodal physiological data can be used to decode error processing, which has been studied, to date, with brain signals only.

Neural and Pupillometric Correlates of Error Perception in an Immersive VR Flight Simulation.

Algorithms detecting erroneous events, as used in brain-computer interfaces, usually rely solely on neural correlates of error perception. The increasing availability of wearable displays with built-in pupillometric sensors enables access to additional physiological data, potentially improving error detection. Hence, we measured both electroencephalographic (EEG) and pupillometric signals of 19 participants while performing a navigation task in an immersive virtual reality (VR) setting.

A Visualisation Driven Training for Vibrotactile Skin Reading.

Vibrotactile skin-reading effectively conveys rich information via vibrotactile patterns, which has gained attention due to recent advancements. However, training to recognize and associate vibrotactile patterns with their meaning is time-consuming and tedious. The conventional training methods use repetitive exposure of the vibrotactile stimuli along with visual and auditory cues of the corresponding symbol.

Comparing driving behavior of humans and autonomous driving in a professional racing simulator.

Motorsports have become an excellent playground for testing the limits of technology, machines, and human drivers. This paper presents a study that used a professional racing simulator to compare the behavior of human and autonomous drivers under an aggressive driving scenario. A professional simulator offers a close-to-real emulation of underlying physics and vehicle dynamics, as well as a wealth of clean telemetry data.

Extended overview techniques for outdoor augmented reality.

In this paper, we explore techniques that aim to improve site understanding for outdoor Augmented Reality (AR) applications. While the first person perspective in AR is a direct way of filtering and zooming on a portion of the data set, it severely narrows overview of the situation, particularly over large areas. We present two interactive techniques to overcome this problem: multi-view AR and variable perspective view.