Harmful algal blooms (HABs) are among the most severe ecological marine problems
worldwide. Under favorable climate and oceanographic conditions, toxin-producing …

Neural network models have achieved state-of-the-art performances in a wide range of
natural language processing (NLP) tasks. However, a long-standing criticism against neural …

Recent years have seen important advances in the quality of state-of-the-art models, but this
has come at the expense of models becoming less interpretable. This survey presents an …

End-to-end neural Natural Language Processing (NLP) models are notoriously difficult to
understand. This has given rise to numerous efforts towards model explainability in recent …

Deep learning as performed by artificial deep neural networks (DNNs) has achieved great
successes recently in many important areas that deal with text, images, videos, graphs, and …

Neural NLP models are increasingly accurate but are imperfect and opaque---they break in
counterintuitive ways and leave end users puzzled at their behavior. Model interpretation …

Adversarial evaluation stress-tests a model's understanding of natural language. Because
past approaches expose superficial patterns, the resulting adversarial examples are limited …

This paper proposes a theoretical analysis of one approach to the eXplainable AI (XAI)
problem, using post-hoc explanation-by-example, that relies on the twinning of artificial …

Despite the rise of decision support systems enabled by artificial intelligence (AI) in
personnel selection, their impact on decision-making processes is largely unknown …

The Empirical Methods in Natural Language Processing (EMNLP) 2018 workshop
BlackboxNLP was dedicated to resources and techniques specifically developed for …