Context: Advancements in machine learning (ML) lead to a shift from the traditional view of
software development, where algorithms are hard-coded by humans, to ML systems …

The increasing usage of machine-learning models in critical domains has recently stressed
the necessity of interpretable machine-learning models. In areas like healthcare, finary–the …

Ethics, explainability, responsibility, and accountability are important concepts for
questioning the societal impacts of artificial intelligence and machine learning (AI), but are …

This paper reflects on a number of trends towards a more open and reproducible approach
to geographic and spatial data science over recent years. In particular, it considers trends …

In recent years, it has been revealed that machine learning models can produce
discriminatory predictions. Hence, fairness protection has come to play a pivotal role in …

Recent advances in computer vision and deep learning have led to breakthroughs in the
development of automated skin image analysis. In particular, skin cancer classification …

In the last decade, researchers have studied fairness as a software property. In particular,
how to engineer fair software systems? This includes specifying, designing, and validating …

In this paper, we investigate the perceptions of AI professionals for their accountability for
mitigating AI bias. Our work is motivated by calls for socially responsible AI development and …

The AI Effect: Working at the Intersection of AI and SE Page 1 FOCUS: GUEST EDITORS’
INTRODUCTION FOCUS: GUEST EDITORS’ INTRODUCTION The AI Effect: Working at the …

The growing ubiquity of machine learning technologies has led to concern and concentrated
efforts at improving data-centric research and practice. While much work has been done on …