The lack of transparency is one of the artificial intelligence (AI)'s fundamental challenges, but
the concept of transparency might be even more opaque than AI itself. Researchers in …

Responsible Artificial Intelligence (RAI) is widely considered as one of the greatest scientific
challenges of our time and is key to increase the adoption of Artificial Intelligence (AI) …

Interpretability in machine learning (ML) is crucial for high stakes decisions and
troubleshooting. In this work, we provide fundamental principles for interpretable ML, and …

Machine learning models in safety-critical settings like healthcare are often “blackboxes”:
they contain a large number of parameters which are not transparent to users. Post-hoc …

Machine learning is an established and frequently used technique in industry and
academia, but a standard process model to improve success and efficiency of machine …

Since its emergence in the 1960s, Artificial Intelligence (AI) has grown to conquer many
technology products and their fields of application. Machine learning, as a major part of the …

Can satisfactory explanations for complex machine learning models be achieved in high-risk
automated decision-making? How can such explanations be integrated into a data …

This paper addresses the black-box problem in artificial intelligence (AI), and the related
problem of explainability of AI in the legal context. We argue, first, that the black box problem …

Since its inception, the choice modelling field has been dominated by theory-driven
modelling approaches. Machine learning offers an alternative data-driven approach for …

The employment of AI systems presents challenges for liability rules. This paper identifies
these challenges and evaluates how liability rules should be adapted in response. The …