Explainable Artificial Intelligence (XAI) has emerged as a crucial research area to address
the interpretability challenges posed by complex machine learning models. In this survey …

Abstract Machine learning models can often fail on subgroups that are
underrepresentedduring training. While dataset balancing can improve performance …

Shortcut learning is when a model–eg a cardiac disease classifier–exploits correlations
between the target label and a spurious shortcut feature, eg a pacemaker, to predict the …

The standard way to teach models is by feeding them lots of data. However, this approach
often teaches models incorrect ideas because they pick up on misleading signals in the …

Abstract Deep Neural Networks (DNNs) are powerful tools for various computer vision tasks
yet they often struggle with reliable uncertainty quantification-a critical requirement for real …

Deep neural classifiers tend to rely on spurious correlations between spurious attributes of
inputs and targets to make predictions, which could jeopardize their generalization …

Spurious correlations are brittle associations between certain attributes of inputs and target
variables, such as the correlation between an image background and an object class. Deep …

Abstract Models for fine-grained image classification tasks, where the difference between
some classes can be extremely subtle and the number of samples per class tends to be low …

The identification and removal of systematic errors in object detectors can be a prerequisite
for their deployment in safety-critical applications like automated driving and robotics. Such …

Machine learning (ML) has demonstrated significant promise in accelerating drug design.
Active ML-guided optimization of therapeutic molecules typically relies on a surrogate model …