Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and
concern among software engineers. To tackle this issue, extensive research has been …

Incorporating machine learning (ML) components into software products raises new
software-engineering challenges and exacerbates existing ones. Many researchers have …

Software engineering is a domain characterized by intricate decision-making processes,
often relying on nuanced intuition and consultation. Recent advancements in deep learning …

Artificial intelligence (AI) presents new challenges for the user experience (UX) of products
and services. Recently, practitioner-facing resources and design guidelines have become …

Large language models that exhibit instruction-following behaviour represent one of the
biggest recent upheavals in conversational interfaces, a trend in large part fuelled by the …

Technology companies continue to invest in efforts to incorporate responsibility in their
Artificial Intelligence (AI) advancements, while efforts to audit and regulate AI systems …

Machine learning models with high accuracy on test data can still produce systematic
failures, such as harmful biases and safety issues, when deployed in the real world. To …

An emerging body of research indicates that ineffective cross-functional collaboration–the
interdisciplinary work done by industry practitioners across roles–represents a major barrier …

Advances in artificial intelligence have enabled unprecedented technical capabilities, yet
making these advances useful in the real world remains challenging. We engaged in a …

Today, many industrial processes are undergoing digital transformation, which often
requires the integration of well-understood domain models and state-of-the-art machine …