As more non-AI experts use complex AI systems for daily tasks, there has been an
increasing effort to develop methods that produce explanations of AI decision making that …

The last decade witnessed an ever-increasing stream of successes in Machine Learning
(ML). These successes offer clear evidence that ML is bound to become pervasive in a wide …

It is essential for users to understand what their AI systems can and can't do in order to use
them safely. However, the problem of enabling users to assess AI systems with sequential …

Despite the surprising power of many modern AI systems that often learn their own
representations, there is significant discontent about their inscrutability and the attendant …

Alignment of human and autonomous agent values and objectives is vital in human-
autonomy teaming settings which require collaborative action toward a common goal. In …

Large Language Models (LLMs) have shown exceptional generative abilities in various
natural language and generation tasks. However, possible anthropomorphization and …

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

While the question of misspecified objectives has gotten much attention in recent years,
most works in this area primarily focus on the challenges related to the complexity of the …

As multi-agent reinforcement learning (MARL) systems are increasingly deployed
throughout society, it is imperative yet challenging for users to understand the emergent …

Generating complex behaviors that satisfy the preferences of non-expert users is a crucial
requirement for AI agents. Interactive reward learning from trajectory comparisons (aka …