Large language models are increasingly capable of generating fluent-appearing text with
relatively little task-specific supervision. But can these models accurately explain …

Explainable artificially intelligent (XAI) systems form part of sociotechnical systems, eg,
human+ AI teams tasked with making decisions. Yet, current XAI systems are rarely …

This paper describes IEEE P7001, a new draft standard on transparency of autonomous
systems. In the paper, we outline the development and structure of the draft standard. We …

Given the uncertainty surrounding how existing explainability methods for autonomous
vehicles (AVs) meet the diverse needs of stakeholders, a thorough investigation is …

Abstract The field of Explainable Artificial Intelligence attempts to solve the problem of
algorithmic opacity. Many terms and notions have been introduced recently to define …

As transparency becomes key for robotics and AI, it will be necessary to evaluate the
methods through which transparency is provided, including automatically generated natural …

Robots are rapidly gaining acceptance in recent times, where the general public, industry
and researchers are starting to understand the utility of robots, for example for delivery to …

Algorithmic decision support systems are widely applied in domains ranging from healthcare
to journalism. To ensure that these systems are fair and accountable, it is essential that …

As AI becomes more integral in our lives, the need for transparency and responsibility
grows. While natural language explanations (NLEs) are vital for clarifying the reasoning …

Network intrusion detection (NID) systems which leverage machine learning have been
shown to have strong performance in practice when used to detect malicious network traffic …