The Utility of “Even if” semifactual explanation to optimise positive outcomes
When users receive either a positive or negative outcome from an automated system,
Explainable AI (XAI) has almost exclusively focused on how to mutate negative outcomes …
Explainable AI (XAI) has almost exclusively focused on how to mutate negative outcomes …
[HTML][HTML] Towards explainable oral cancer recognition: Screening on imperfect images via Informed Deep Learning and Case-Based Reasoning
M Parola, FA Galatolo, G La Mantia… - … Medical Imaging and …, 2024 - Elsevier
Oral squamous cell carcinoma recognition presents a challenge due to late diagnosis and
costly data acquisition. A cost-efficient, computerized screening system is crucial for early …
costly data acquisition. A cost-efficient, computerized screening system is crucial for early …
Explaining deep neural networks by leveraging intrinsic methods
B La Rosa - arXiv preprint arXiv:2407.12243, 2024 - arxiv.org
Despite their impact on the society, deep neural networks are often regarded as black-box
models due to their intricate structures and the absence of explanations for their decisions …
models due to their intricate structures and the absence of explanations for their decisions …
Probable-class Nearest-neighbor Explanations Improve AI & Human Accuracy
Nearest neighbors (NN) have traditionally been used both for making final decisions—such
as in Support Vector Machines or $ k $-NN classifiers—and for providing users with …
as in Support Vector Machines or $ k $-NN classifiers—and for providing users with …