Explainable AI (XAI) is widely viewed as a sine qua non for ever-expanding AI research. A
better understanding of the needs of XAI users, as well as human-centered evaluations of …

The promise of multimodal models for real-world applications has inspired research in
visualizing and understanding their internal mechanics with the end goal of empowering …

While a lot of research in explainable AI focuses on producing effective explanations, less
work is devoted to the question of how people understand and interpret the explanation. In …

This chapter delves into the recent accountability tools tailored for the evolving landscape of
machine learning models for complex reasoning tasks. With the increasing integration of …

Modern machine learning models are opaque, and as a result there is a burgeoning
academic subfield on methods that explain these models' behavior. However, what is the …

Saliency maps can explain a neural model's predictions by identifying important input
features. They are difficult to interpret for laypeople, especially for instances with many …

The human-centric explainable artificial intelligence (HCXAI) community has raised the
need for framing the explanation process as a conversation between human and machine …

Due to convenience, open-source software is widely used. For beneficial reasons, open-
source maintainers often fix the vulnerabilities silently, exposing their users unaware of the …

Recent advances in eXplainable AI (XAI) have provided new insights into how models for
vision, language, and tabular data operate. However, few approaches exist for …

Recent advances in AI models have increased the integration of AI-based decision aids into
the human decision making process. To fully unlock the potential of AI-assisted decision …