Development of mechatronic systems requires collaboration among experts from different
design domains. In this paper the authors identify a set of challenges related to the design of …

The growing capability and accessibility of machine learning has led to its application to many
real-world domains and data about people. Despite the benefits algorithmic systems may …

Studies of cetacean evolution using genetics and other biomolecules have come a long way—from
the use of allozymes and short sequences of mitochondrial or nuclear DNA to the …

… Lastly, we built a set of visualizations for analyzing model performance across intersectional
subgroups (G) based on a system by Cabrera et al. [9]. The visualization can help users …

The COVID-19 pandemic presented enormous data challenges in the United States. Policy
makers, epidemiological modelers, and health researchers all require up-to-date data on the …

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 detect and …

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 …

Ensuring effective public understanding of algorithmic decisions that are powered by machine
learning techniques has become an urgent task with the increasing deployment of AI …

AI systems can fail to learn important behaviors, leading to real-world issues like safety
concerns and biases. Discovering these systematic failures often requires significant developer …

… The University of Groningen provided the funding for Andrea Cabrera's PhD. We would also
like to thank Brian Lavin, Hielko van der Hoorn, the five anonymous reviewers and the editor…