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Found 6 papers, 1 papers with code
Concept-based Explainable Artificial Intelligence: A Survey
The field of explainable artificial intelligence emerged in response to the growing need for more transparent and reliable models.
Conversational Data Exploration: A Game-Changer for Designing Data Science Pipelines
This paper proposes a conversational approach implemented by the system Chatin for driving an intuitive data exploration experience.
Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
In this context, we analyze the burned area severity estimation problem by exploiting a state-of-the-art deep learning framework.
Explaining the Deep Natural Language Processing by Mining Textual Interpretable Features
Despite the high accuracy offered by state-of-the-art deep natural-language models (e. g. LSTM, BERT), their application in real-life settings is still widely limited, as they behave like a black-box to the end-user.
What's in the box? Explaining the black-box model through an evaluation of its interpretable features
Algorithms are powerful and necessary tools behind a large part of the information we use every day.
Automating concept-drift detection by self-evaluating predictive model degradation
To this aim, suitable automatic solutions to self-assess the prediction quality and the data distribution drift between the original training set and the new data have to be devised.
