Results 31 to 40 of about 81,501 (265)
Explainable AI for designers: A human-centered perspective on mixed-initiative co-creation [PDF]
, 2018 Growing interest in eXplainable Artificial Intelligence (XAI) aims to make AI and machine learning more understandable to human users. However, most existing work focuses on new algorithms, and not on usability, practical interpretability and efficacy on
Bidarra, Rafael +4 more
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Biomedicines, 2022 Diagnostic support tools based on artificial intelligence (AI) have exhibited high performance in various medical fields. However, their clinical application remains challenging because of the lack of explanatory power in AI decisions (black box problem),
Akira Sakai +12 more
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A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation
, 2020 Traditionally, abnormal heart sound classification is framed as a three-stage process. The first stage involves segmenting the phonocardiogram to detect fundamental heart sounds; after which features are extracted and classification is performed.
Denman, Simon +5 more
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Exploring Explainable Artificial Intelligence for Transparent Decision Making [PDF]
E3S Web of Conferences, 2023 Artificial intelligence (AI) has become a potent tool in many fields, allowing complicated tasks to be completed with astounding effectiveness. However, as AI systems get more complex, worries about their interpretability and transparency have become ...
Praveenraj D. David Winster +6 more
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Explainability of Automated Fact Verification Systems: A Comprehensive Review
Applied Sciences, 2023 The rapid growth in Artificial Intelligence (AI) has led to considerable progress in Automated Fact Verification (AFV). This process involves collecting evidence for a statement, assessing its relevance, and predicting its accuracy.
Manju Vallayil +3 more
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Opening the Black Box of Financial AI with CLEAR-Trade: A CLass-Enhanced Attentive Response Approach for Explaining and Visualizing Deep Learning-Driven Stock Market Prediction [PDF]
, 2017 Deep learning has been shown to outperform traditional machine learning algorithms across a wide range of problem domains. However, current deep learning algorithms have been criticized as uninterpretable "black-boxes" which cannot explain their decision
Kumar, Devinder +2 more
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, 2019 In today's legal environment, lawsuits and regulatory investigations require companies to embark upon increasingly intensive data-focused engagements to identify, collect and analyze large quantities of data.
Chhatwal, Rishi +5 more
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Diagnostics, 2021 Background: Although several studies have been launched towards the prediction of risk factors for mortality and admission in the intensive care unit (ICU) in COVID-19, none of them focuses on the development of explainable AI models to define an ICU ...
Vasileios C. Pezoulas +12 more
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Increasing trust and fairness in machine learning applications within the mortgage industry
Machine Learning with Applications, 2022 The integration of machine learning in applications provides opportunities for increased efficiency in many organisations. However, the deployment of such systems is often hampered by the lack of insight into how their decisions are reached, resulting in
W. van Zetten, G.J. Ramackers, H.H. Hoos
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Review of explainable artificial intelligence and its application prospect in earthquake science
地震科学进展In the past decade, Artificial Intelligence (AI), as an important branch of computer science, has made breakthroughs in the research fields of computer vision, natural language processing, machine translation and so on.
Lihong Huang +11 more
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