WaSP-ECG: A Wave Segmentation Pretraining Toolkit for Electrocardiogram Analysis
Frontiers in Physiology, 2022 IntroductionRepresentation learning allows artificial intelligence (AI) models to learn useful features from large, unlabelled datasets. This can reduce the need for labelled data across a range of downstream tasks.
Rob Brisk +8 more
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Painting the Black Box White: Experimental Findings from Applying XAI to an ECG Reading Setting
Machine Learning and Knowledge Extraction, 2023 The emergence of black-box, subsymbolic, and statistical AI systems has motivated a rapid increase in the interest regarding explainable AI (XAI), which encompasses both inherently explainable techniques, as well as approaches to make black-box AI ...
Federico Cabitza +5 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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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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Artificial Intelligence for the Financial Services Industry: What Challenges Organizations to Succeed? [PDF]
, 2019 As a research field, artificial intelligence (AI) exists for several years. More recently, technological breakthroughs, coupled with the fast availability of data, have brought AI closer to commercial use. Internet giants such as Google, Amazon, Apple or
Beck, Roman +2 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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Can You Explain That? Lucid Explanations Help Human-AI Collaborative Image Retrieval
, 2019 While there have been many proposals on making AI algorithms explainable, few have attempted to evaluate the impact of AI-generated explanations on human performance in conducting human-AI collaborative tasks.
Burachas, Giedrius +4 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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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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, 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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