44 citations · 113 across the 13 of their papers we have counts for
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cs.LG2022
Explaining Predictions from Machine Learning Models: Algorithms, Users, and Pedagogy
Ana Lucic
Model explainability has become an important problem in machine learning (ML) due to the increased effect that algorithmic predictions have on humans. Explanations can help users u…
cs.LG2022★ 1 cited
Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Users
Ana Lucic, Sheeraz Ahmad, Amanda Furtado Brinhosa +7
When using medical images for diagnosis, either by clinicians or artificial intelligence (AI) systems, it is important that the images are of high quality. When an image is of low…
cs.CL2022★ 1 cited
A Song of (Dis)agreement: Evaluating the Evaluation of Explainable Artificial Intelligence in Natural Language Processing
Michael Neely, Stefan F. Schouten, Maurits Bleeker +1
There has been significant debate in the NLP community about whether or not attention weights can be used as an explanation - a mechanism for interpreting how important each input…