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Found 7 papers, 1 papers with code
DARE: Towards Robust Text Explanations in Biomedical and Healthcare Applications
This results in our DomainAdaptiveAREstimator (DARE) attribution robustness estimator, allowing us to properly characterize the domain-specific robustness of faithful explanations.
Auditing and Generating Synthetic Data with Controllable Trust Trade-offs
We introduce a holistic auditing framework that comprehensively evaluates synthetic datasets and AI models.
Estimating the Adversarial Robustness of Attributions in Text with Transformers
Finally, with experiments on several text classification architectures, we show that TEA consistently outperforms current state-of-the-art AR estimators, yielding perturbations that alter explanations to a greater extent while being more fluent and less perceptible.
Fooling Explanations in Text Classifiers
TEF can significantly decrease the correlation between unchanged and perturbed input attributions, which shows that all models and explanation methods are susceptible to TEF perturbations.
Artificial Intelligence Decision Support for Medical Triage
Applying state-of-the-art machine learning and natural language processing on approximately one million of teleconsultation records, we developed a triage system, now certified and in use at the largest European telemedicine provider.
FAR: A General Framework for Attributional Robustness
Therefore, we define a novel generic framework for attributional robustness (FAR) as general problem formulation for training models with robust attributions.
Patient Risk Assessment and Warning Symptom Detection Using Deep Attention-Based Neural Networks
In addition, a method to detect warning symptoms is implemented to render the classification task transparent from a medical perspective.
