The continuous advancement of Artificial Intelligence (AI) has been revolutionizing the
strategy of decision-making in different life domains. Regardless of this achievement, AI …

Explanations have gained an increasing level of interest in the AI and Machine Learning
(ML) communities in order to improve model transparency and allow users to form a mental …

Chemists can be skeptical in using deep learning (DL) in decision making, due to the lack of
interpretability in “black-box” models. Explainable artificial intelligence (XAI) is a branch of …

External data sources are increasingly being used to train machine learning (ML) models as
the data demand increases. However, the integration of external data into training poses …

Part-prototype Networks (ProtoPNets) are concept-based classifiers designed to achieve the
same performance as black-box models without compromising transparency. ProtoPNets …

We present a novel rationale-centric framework with human-in-the-loop--Rationales-centric
Double-robustness Learning (RDL)--to boost model out-of-distribution performance in few …

Despite being highly performant, deep neural networks might base their decisions on
features that spuriously correlate with the provided labels, thus hurting generalization. To …

As machine learning models become larger, and are increasingly trained on large and
uncurated datasets in weakly supervised mode, it becomes important to establish …

Identifying spurious correlations learned by a trained model is at the core of refining a
trained model and building a trustworthy model. We present a simple method to identify …

Neural networks (NNs) can learn to rely on spurious signals in the training data, leading to
poor generalisation. Recent methods tackle this problem by training NNs with additional …