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How explainable AI affects human performance: A systematic review of the behavioural consequences of saliency maps
R Müller - International Journal of Human–Computer Interaction, 2024 - Taylor & Francis
Saliency maps can explain how deep neural networks classify images. But are they actually
useful for humans? The present systematic review of 68 user studies found that while …
useful for humans? The present systematic review of 68 user studies found that while …
[HTML][HTML] Evidence-based XAI: An empirical approach to design more effective and explainable decision support systems
This paper proposes a user study aimed at evaluating the impact of Class Activation Maps
(CAMs) as an eXplainable AI (XAI) method in a radiological diagnostic task, the detection of …
(CAMs) as an eXplainable AI (XAI) method in a radiological diagnostic task, the detection of …
Invisible to machines: designing AI that supports vision work in radiology
In this article we provide an analysis focusing on clinical use of two deep learning-based
automatic detection tools in the field of radiology. The value of these technologies conceived …
automatic detection tools in the field of radiology. The value of these technologies conceived …
[HTML][HTML] Explainability and uncertainty: Two sides of the same coin for enhancing the interpretability of deep learning models in healthcare
Background The increasing use of Deep Learning (DL) in healthcare has highlighted the
critical need for improved transparency and interpretability. While Explainable Artificial …
critical need for improved transparency and interpretability. While Explainable Artificial …
Algorithmic Authority & AI Influence in Decision Settings: Theories and Implications for Design
This workshop explores the influence of AI systems on human decision-making-algorithmic
authority-and the broader concept of technology dominance, which includes both positive …
authority-and the broader concept of technology dominance, which includes both positive …