Results 11 to 20 of about 36,247 (313)
System-And-Software-Requirements-In-Relation-To-Observability-And-Explainability [PDF]
, 2023 Software maintenance and evolution are crucial aspects of software development. In today's world, observability and explainability are becoming essential requirements for software systems.
Sandeep Kampa (15474647)
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Does Explainability Require Transparency? [PDF]
, 2022 Dealing with opaque algorithms, the frequent overlap between transparency and explainability produces seemingly unsolvable dilemmas, as the much-discussed trade-off between model performance and model transparency. Referring to Niklas Luhmann's notion of
Esposito, Elena, Elena Esposito
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Explainability in medicine in an era of AI-based clinical decision support systems
Frontiers in Genetics, 2022 The combination of “Big Data” and Artificial Intelligence (AI) is frequently promoted as having the potential to deliver valuable health benefits when applied to medical decision-making.
Robin L. Pierce +8 more
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Towards explainability in knowledge enhanced neural networks [PDF]
, 2022 Research on Deep Learning has achieved remarkable results in recent years, mainly thanks to the computing power of modern computers and the increasing availability of large data sets.
Mazzieri, Riccardo
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XClusters: Explainability-first Clustering
, 2022 We study the problem of explainability-first clustering where explainability becomes a first-class citizen for clustering. Previous clustering approaches use decision trees for explanation, but only after the clustering is completed.
Hwang, Hyunseung, Whang, Steven Euijong
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Foundations of fine-grained explainability [PDF]
, 2021 Explainability is the process of linking part of the inputs given to a calculation to its output, in such a way that the selected inputs somehow “cause” the result.
Tremblay, Hugo +3 more
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Explainability in Graph Neural Networks
, 2022 Deep neural networks have been predominant in AI applications during the past decade. Inspired by the success of deep learning in image and text domains, graph neural networks (GNNs) have been extensively developed for graphs in various applications ...
Li, Peibo
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Explainability in Cybersecurity Data Science
, 2023 Cybersecurity is data-rich and therefore a natural setting for machine learning (ML). However, many challenges hamper ML deployment into cybersecurity systems and organizations.
Jeffrey Mellon (8419557) +1 more
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Journal of Perinatal Education, 2005 Childbirth educator humorously discusses props used as tools for teaching and teasing.
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Can Explainable AI Explain Unfairness? A Framework for Evaluating Explainable AI
CoRR, 2021 Many ML models are opaque to humans, producing decisions too complex for humans to easily understand. In response, explainable artificial intelligence (XAI) tools that analyze the inner workings of a model have been created. Despite these tools' strength in translating model behavior, critiques have raised concerns about the impact of XAI tools as a ...
Kiana Alikhademi +3 more
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