Machine learning plays a role in many deployed decision systems, often in ways that are
difficult or impossible to understand by human stakeholders. Explaining, in a human …

Causal inference has recently garnered significant interest among recommender system
(RS) researchers due to its ability to dissect cause-and-effect relationships and its broad …

Counterfactual explanations have shown promising results as a post-hoc framework to make
image classifiers more explainable. In this paper, we propose DiME, a method allowing the …

Deep Learning has reached human-level performance in several medical tasks including
classification of histopathological images. Continuous effort has been made at finding …

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 …

Counterfactual explanations are a common tool to explain artificial intelligence models. For
Reinforcement Learning (RL) agents, they answer" Why not?" or" What if?" questions by …

Counterfactual explanations elucidate algorithmic decisions by pointing to scenarios that
would have led to an alternative, desired outcome. Giving insight into the model's behavior …

Tabular medical datasets, like electronic health records (EHRs), biobanks, and structured
clinical trial data, are rich sources of information with the potential to advance precision …

Interpretable time series prediction is crucial for safety-critical areas such as healthcare and
autonomous driving. Most existing methods focus on interpreting predictions by assigning …

Algorithms are increasingly used to automate large-scale decision-making processes, eg,
online platforms that make instant decisions in lending, hiring, and education. When such …