Interpretability, explainability, and transparency are key issues to introducing artificial
intelligence methods in many critical domains. This is important due to ethical concerns and …

Broad-XAI moves away from interpreting individual decisions based on a single datum and
aims to provide integrated explanations from multiple machine learning algorithms into a …

With advances in reinforcement learning (RL), agents are now being developed in high-
stakes application domains such as healthcare and transportation. Explaining the behavior …

We present a user study to investigate the impact of explanations on non-experts'
understanding of reinforcement learning (RL) agents. We investigate both a common RL …

In recent years, reinforcement learning (RL) systems have shown impressive performance
and remarkable achievements. Many achievements can be attributed to combining RL with …

How should reinforcement learning (RL) agents explain themselves to humans not trained in
AI? To gain insights into this question, we conducted a 124-participant, four-treatment …

Robotic systems are more present in our society everyday. In human–robot environments, it
is crucial that end-users may correctly understand their robotic team-partners, in order to …

In this paper, we pose a new challenge for AI researchers–to develop intelligent systems
that support justified agency. We illustrate this ability with examples and relate it to two more …

In the last decades, Reinforcement Learning (RL) started entering every-days life since its
super-human performances. Recently, RL has been applied to problems related to …

Modern black-box artificial intelligence algorithms are computationally powerful yet fallible in
unpredictable ways. While much research has gone into developing techniques to interpret …