While the opportunities of ML and AI in healthcare are promising, the growth of complex data-
driven prediction models requires careful quality and applicability assessment before they …

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 …

AI-based systems are software systems with functionalities enabled by at least one AI
component (eg, for image-, speech-recognition, and autonomous driving). AI-based systems …

The introduction of machine learning (ML) components in software projects has created the
need for software engineers to collaborate with data scientists and other specialists. While …

While artificial intelligence (AI) governance is thoroughly discussed on a philosophical,
societal, and regulatory level, few works target companies. We address this gap by deriving …

Although numerous ethical principles and guidelines have been proposed to guide the
development of artificial intelligence (AI) systems, it has proven difficult to translate these …

Tech-leading organizations are embracing the forthcoming artificial intelligence revolution.
Intelligent systems are replacing and cooperating with traditional software components …

Machine learning components are essential for today's software systems, causing a need to
adapt traditional software engineering practices when developing machine-learning-based …

Data scientists require rich mental models of how AI systems behave to effectively train,
debug, and work with them. Despite the prevalence of AI analysis tools, there is no general …

Artificial intelligence (AI) offers much promise for improving healthcare. However, it runs the
looming risk of causing individual and societal harms; for instance, exacerbating inequalities …