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   Articles with "explainability" as a keyword



Artificial intelligence in pharmacovigilance: Do we need explainability?

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recommendations! Published in 2022 at "Pharmacoepidemiology and Drug Safety"

DOI: 10.1002/pds.5501

Abstract: With increasing deployment of complex and opaque machine learning algorithms (black boxes) to make decisions in areas that profoundly affect individuals such as underwriting, judicial sentencing, and robotic driving, are increasing calls for explanations of… read more here.

Keywords: explainability; black boxes; intelligence pharmacovigilance; artificial intelligence ... See more keywords

Finding the input features that reduce the entropy of a neural network’s prediction

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recommendations! Published in 2024 at "Applied Intelligence"

DOI: 10.1007/s10489-024-05277-5

Abstract: In deep learning-based image classification, the entropy of a neural network’s output is often taken as a measure of its uncertainty. We introduce an explainability method that identifies those features in the input that impact… read more here.

Keywords: finding input; explainability; prediction; neural network ... See more keywords

Logging requirement for continuous auditing of responsible machine learning-based applications

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recommendations! Published in 2025 at "Empirical Software Engineering"

DOI: 10.1007/s10664-025-10656-8

Abstract: Machine learning (ML) is increasingly used across various industries to automate decision-making processes. However, concerns about the ethical and legal compliance of ML models have arisen due to their lack of transparency, fairness, and accountability.… read more here.

Keywords: explainability; based applications; machine learning; logging ... See more keywords

Rethinking explainability: toward a postphenomenology of black-box artificial intelligence in medicine

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recommendations! Published in 2022 at "Ethics and Information Technology"

DOI: 10.1007/s10676-022-09631-4

Abstract: In recent years, increasingly advanced artificial intelligence (AI), and in particular machine learning, has shown great promise as a tool in various healthcare contexts. Yet as machine learning in medicine has become more useful and… read more here.

Keywords: explainability; black box; medicine; artificial intelligence ... See more keywords

Clinical Explainability Failure (CEF) & Explainability Failure Ratio (EFR) – Changing the Way We Validate Classification Algorithms

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recommendations! Published in 2022 at "Journal of Medical Systems"

DOI: 10.1007/s10916-022-01806-2

Abstract: Adoption of Artificial Intelligence (AI) algorithms into the clinical realm will depend on their inherent trustworthiness, which is built not only by robust validation studies but is also deeply linked to the explainability and interpretability… read more here.

Keywords: explainability; failure ratio; ratio efr; explainability failure ... See more keywords

S-SIRUS: an explainability algorithm for spatial regression Random Forest

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recommendations! Published in 2024 at "Statistics and Computing"

DOI: 10.1007/s11222-025-10656-0

Abstract: Random Forest (RF) is a widely used machine learning algorithm known for its flexibility, user-friendliness, and high predictive performance across various domains. However, it is non-interpretable. This can limit its usefulness in applied sciences, where… read more here.

Keywords: explainability; random forest; spatial correlation; regression ... See more keywords
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Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability

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recommendations! Published in 2020 at "Science and Engineering Ethics"

DOI: 10.1007/s11948-019-00146-8

Abstract: This paper discusses the problem of responsibility attribution raised by the use of artificial intelligence (AI) technologies. It is assumed that only humans can be responsible agents; yet this alone already raises many issues, which… read more here.

Keywords: justification explainability; responsibility attribution; artificial intelligence; responsibility ... See more keywords

Estimation of Task-Related Dynamic Brain Connectivity via Data Inflation and Classification Model Explainability

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recommendations! Published in 2025 at "Neuroinformatics"

DOI: 10.1007/s12021-025-09733-6

Abstract: Study of brain function often involves analyzing task-related switching between intrinsic brain networks, which connect various brain regions. Functional brain connectivity analysis methods aim to estimate these networks but are limited by the statistical constraints… read more here.

Keywords: classification; brain connectivity; explainability; task related ... See more keywords

Explainability of CNN-based Alzheimer’s disease detection from online handwriting

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recommendations! Published in 2024 at "Scientific Reports"

DOI: 10.1038/s41598-024-72650-2

Abstract: With over 55 million people globally affected by dementia and nearly 10 million new cases reported annually, Alzheimer’s disease is a prevalent and challenging neurodegenerative disorder. Despite significant advancements in machine learning techniques for Alzheimer’s… read more here.

Keywords: alzheimer disease; disease; disease detection; explainability ... See more keywords

Simulating clinical features on chest radiographs for medical image exploration and CNN explainability using a style-based generative adversarial autoencoder

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recommendations! Published in 2024 at "Scientific Reports"

DOI: 10.1038/s41598-024-75886-0

Abstract: Explainability of convolutional neural networks (CNNs) is integral for their adoption into radiological practice. Commonly used attribution methods localize image areas important for CNN prediction but do not characterize relevant imaging features underlying these areas,… read more here.

Keywords: see gaan; cnn; exploration; clinical features ... See more keywords

Medical slice transformer for improved diagnosis and explainability on 3D medical images with DINOv2

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recommendations! Published in 2025 at "Scientific Reports"

DOI: 10.1038/s41598-025-09041-8

Abstract: Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are essential clinical cross-sectional imaging techniques for diagnosing complex conditions. However, large 3D datasets with annotations for deep learning are scarce. While methods like DINOv2 are encouraging… read more here.

Keywords: slice transformer; explainability; medical slice; medical images ... See more keywords