Explainable Artificial Intelligence (XAI) [PDF]
Information Technology and Libraries, 2022 The field of explainable artificial intelligence (XAI) advances techniques, processes, and strategies that provide explanations for the predictions, recommendations, and decisions of opaque and complex machine learning systems.
Ranu Sewada +3 more
semanticscholar +2 more sources
Explainable Artificial Intelligence (XAI) in Insurance
Risks, 2022 Explainable Artificial Intelligence (XAI) models allow for a more transparent and understandable relationship between humans and machines. The insurance industry represents a fundamental opportunity to demonstrate the potential of XAI, with the industry ...
Emer Owens +5 more
doaj +3 more sources
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
IEEE Access, 2018 At the dawn of the fourth industrial revolution, we are witnessing a fast and widespread adoption of artificial intelligence (AI) in our daily life, which contributes to accelerating the shift towards a more algorithmic society.
Amina Adadi, Mohammed Berrada
exaly +2 more sources
Explainable artificial intelligence (XAI) in medical imaging: a systematic review of techniques, applications, and challenges. [PDF]
BMC Med ImagingExplainable Artificial Intelligence (XAI) is crucial for enhancing transparency and trustworthiness, as well as for developing AI-based diagnostic systems in medical imaging to achieve clinical acceptability and reliability.
Ahmed F +5 more
europepmc +2 more sources
Evaluating Explainable Artificial Intelligence (XAI) techniques in chest radiology imaging through a human-centered Lens. [PDF]
PLoS OneThe field of radiology imaging has experienced a remarkable increase in using of deep learning (DL) algorithms to support diagnostic and treatment decisions.
E Ihongbe I +4 more
europepmc +2 more sources
A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging. [PDF]
J ImagingThe combination of medical imaging and deep learning has significantly improved diagnostic and prognostic capabilities in the healthcare domain. Nevertheless, the inherent complexity of deep learning models poses challenges in understanding their ...
Bhati D, Neha F, Amiruzzaman M.
europepmc +2 more sources
A Survey of Explainable Artificial Intelligence (XAI) in Financial Time Series Forecasting [PDF]
ACM Computing SurveysArtificial intelligence (AI) models have reached a very significant level of accuracy. While their superior performance offers considerable benefits, their inherent complexity often decreases human trust, which slows their application in high-risk ...
Shengrui Wang
exaly +2 more sources
Explainable artificial intelligence (XAI) in finance: a systematic literature review
Artificial Intelligence ReviewAs the range of decisions made by Artificial Intelligence (AI) expands, the need for Explainable AI (XAI) becomes increasingly critical. The reasoning behind the specific outcomes of complex and opaque financial models requires a thorough justification ...
Audrius Kabašinskas +1 more
exaly +2 more sources
The application of explainable artificial intelligence (XAI) in electronic health record research: A scoping review. [PDF]
Digit HealthMachine Learning (ML) and Deep Learning (DL) models show potential in surpassing traditional methods including generalised linear models for healthcare predictions, particularly with large, complex datasets.
Caterson J, Lewin A, Williamson E.
europepmc +2 more sources