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 …

Mitigating bias in algorithmic systems is a critical issue drawing attention across
communities within the information and computer sciences. Given the complexity of the …

While Deep Neural Networks (DNNs) are deriving the major innovations through their
powerful automation, we are also witnessing the peril behind automation as a form of bias …

Deep learning models for image classification suffer from dangerous issues often
discovered after deployment. The process of identifying bugs that cause these issues …

The automatic detection of conflictual languages (harmful, aggressive, abusive, and
offensive languages) is essential to provide a healthy conversation environment on the Web …

As the performance and complexity of machine learning models have grown significantly
over the last years, there has been an increasing need to develop methodologies to …

Despite the impressive performance of Artificial Intelligence (AI) systems, their robustness
remains elusive and constitutes a key issue that impedes large-scale adoption. Besides …

Unknown unknowns represent a major challenge in reliable image recognition. Existing
methods mainly focus on unknown unknowns identification, leveraging human intelligence …

Clinicians increasingly pay attention to Artificial Intelligence (AI) to improve the quality and
timeliness of their services. There are converging opinions on the need for Explainable AI …

Abstract The application of Deep Neural Networks (DNNs) to a broad variety of tasks
demands methods for co** with the complex and opaque nature of these architectures …