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Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions
As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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.
Responsible AI in Healthcare
This article discusses open problems, implemented solutions, and future research in the area of responsible AI in healthcare.
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.
A giant with feet of clay: on the validity of the data that feed machine learning in medicine
This paper considers the use of Machine Learning (ML) in medicine by focusing on the main problem that this computational approach has been aimed at solving or at least minimizing: uncertainty.
Breeding electric zebras in the fields of Medicine
A few notes on the use of machine learning in medicine and the related unintended consequences.
