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Painting the black box white: experimental findings from applying XAI to an ECG reading setting
The shift from symbolic AI systems to black-box, sub-symbolic, and statistical ones has motivated a rapid increase in the interest toward explainable AI (XAI), i. e. approaches to make black-box AI systems explainable to human decision makers with the aim of making these systems more acceptable and more usable tools and supports.
Everything is Varied: The Surprising Impact of Individual Variation on ML Robustness in Medicine
In medical settings, Individual Variation (IV) refers to variation that is due not to population differences or errors, but rather to within-subject variation, that is the intrinsic and characteristic patterns of variation pertaining to a given instance or the measurement process.
A Distributional Approach for Soft Clustering Comparison and Evaluation
The development of external evaluation criteria for soft clustering (SC) has received limited attention: existing methods do not provide a general approach to extend comparison measures to SC, and are unable to account for the uncertainty represented in the results of SC algorithms.
Toward a Perspectivist Turn in Ground Truthing for Predictive Computing
Most Artificial Intelligence applications are based on supervised machine learning (ML), which ultimately grounds on manually annotated data.
Who wants accurate models? Arguing for a different metrics to take classification models seriously
With the increasing availability of AI-based decision support, there is an increasing need for their certification by both AI manufacturers and notified bodies, as well as the pragmatic (real-world) validation of these systems.
