In recent years, machine learning has transitioned from a field of academic research interest
to a field capable of solving real-world business problems. However, the deployment of …

For organizations, the development of new business models and competitive advantages
through the integration of artificial intelligence (AI) in business and IT strategies holds …

Datasets that power machine learning are often used, shared, and reused with little visibility
into the processes of deliberation that led to their creation. As artificial intelligence systems …

The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …

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 …

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 …

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 …

In recent years, several new technical methods have been developed to make AI-models
more transparent and interpretable. These techniques are often referred to collectively as 'AI …

This work identifies 18 foundational challenges in assuring the alignment and safety of large
language models (LLMs). These challenges are organized into three different categories …

Machine Learning (ML) is now used in a range of systems with results that are reported to
exceed, under certain conditions, human performance. Many of these systems, in domains …