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Proceedings of the 25th International Conference on Intelligent User Interfaces, 2020 Explainable AI (XAI) has started experiencing explosive growth, echoing the explosive growth that has preceded it of AI becoming used for practical purposes that impact the general public. This spread of AI into the world outside of research labs brings with it pressures and requirements that many of us have perhaps not thought about deeply enough.
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A mental models approach for defining explainable artificial intelligence
BMC Medical Informatics and Decision Making, 2021 Background Wide-ranging concerns exist regarding the use of black-box modelling methods in sensitive contexts such as healthcare. Despite performance gains and hype, uptake of artificial intelligence (AI) is hindered by these concerns.
Michael Merry, Pat Riddle, Jim Warren
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Quantitative Explainable AI For Face Recognition
, 2023 Face recognition is widely adopted in our daily life in recent years. It usually relies on sophisticated techniques to achieve high accuracy in identifying or verifying the identities of given face images.
Peng, S, Dong, N, Bai, G
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Proceedings of the Conference on Fairness, Accountability, and Transparency, 2019 Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might
Brent D. Mittelstadt 0002 +2 more
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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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The data ethics challenges of explainable AI and their knowledge-based solutions
, 2021 . Explainable AI has recently gained momentum as an approach to overcome some of the more obvious ethical implications of the increasingly widespread application of AI (mostly machine learning).
d\u27Aquin, Mathieu, d'Aquin, Mathieu
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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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Explainable AI, but explainable to whom?
CoRR, 2021 Book chapter for AI in ...
Julie Gerlings +2 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 Software Engineering
, 2021 We are very proud to release the first version of this Explainable AI for Software Engineering book. This book consists of three parts: Part 1-Explainable AI: We first provide a concise yet essential introduction to the most important aspects of ...
Jirayus Jiarpakdee +1 more
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