Transparency in AI models is essential for promoting human–AI collaboration and ensuring
regulatory compliance. However, interpreting these models is a complex process influenced …

We propose a novel approach to human interaction with artificial intelligence systems (HAII),
alternative to the mainstream dyadic one where humans and AI are seen as interacting …

Reinforcement Learning performs well in many different application domains and is starting
to receive greater authority and trust from its users. But most people are unfamiliar with how …

Explainability has been a core research topic in AI for decades and therefore it is surprising
that the current concept of Explainable AI (XAI) seems to have been launched as late as …

As artificial intelligence (AI) thrives and propagates through modern life, a key question to
ask is how to include humans in future AI? Despite human involvement at every stage of the …

The growing need for in-depth analysis of predictive models leads to a series of new
methods for explaining their local and global properties. Which of these methods is the best …

Abstract Theory of Mind is commonly defined as the ability to attribute mental states (eg,
beliefs, goals) to oneself, and to others. A large body of previous work—from the social …

Explainability has been a challenge in AI for as long as AI has existed. With the recently
increased use of AI in society, it has become more important than ever that AI systems would …

Vehicular network plays a major role in understanding the detail study of vehicle
communications. Multiple vehicles in local communication range need to exchange the …

Autonomous vehicles are subject to skepticism from the general public due to reports of fatal
accidents and a lack of trust in the technology. Yet these vehicles are predicted to have …