The literature on artificial intelligence (AI) or machine learning (ML) in mental health and
psychiatry lacks consensus on what “explainability” means. In the more general XAI …

Future connected and autonomous vehicles (CAVs) must be secured against cyberattacks
for their everyday functions on the road so that safety of passengers and vehicles can be …

Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated
applications, but the outcomes of many AI models are challenging to comprehend and trust …

We devise deep nearest centroids (DNC), a conceptually elegant yet surprisingly effective
network for large-scale visual recognition, by revisiting Nearest Centroids, one of the most …

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

In recent years, the field of explainable AI (XAI) has produced a vast collection of algorithms,
providing a useful toolbox for researchers and practitioners to build XAI applications. With …

Smart grids are rapidly replacing conventional networks on a worldwide scale. A smart grid
has drawbacks, just like any other novel technology. A smart grid cyberattack is one of the …

As AI systems demonstrate increasingly strong predictive performance, their adoption has
grown in numerous domains. However, in high-stakes domains such as criminal justice and …

In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if
harnessed appropriately, may deliver the best of expectations over many application sectors …

The notion of uncertainty is of major importance in machine learning and constitutes a key
element of machine learning methodology. In line with the statistical tradition, uncertainty …