Results 221 to 230 of about 64,893 (276)

xGNN4MI: explainability of graph neural networks in 12-lead electrocardiography for cardiovascular disease classification. [PDF]

NPJ Digit Med
Maurer MC, Hempel P, Steinhaus KE, Chereda H, Vollmer M, Krefting D, Spicher N, Hauschild AC.   +7 more
europepmc   +1 more source

Response to the Letter to the Editor. [PDF]

Forensic Sci Int Synerg
Karie NM.
europepmc   +1 more source

Explainable AI Approaches in Federated Learning: Systematic Review. [PDF]

JMIR AI
Tunduny T, Shibwabo B.
europepmc   +1 more source

Transparent AI for mathematics: transformer-based large language models for mathematical entity relationship extraction with XAI. [PDF]

Sci Rep
Aurpa TT.
europepmc   +1 more source

AI-Driven Mental Health Support for Caregivers of Individuals With Alzheimer Disease: Systematic Literature Review and Development of a Conceptual Framework.

JMIR Ment Health
Salma SU, Renduchintala CR, Siddique I, Sterling E, Sneha S, Sakib N.   +5 more
europepmc   +1 more source

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Explaining Explainable AI

2020
An aspect of User friendly AI involves explanation and better transparency of AI. Explainable AI(XAI) is an emerging area of research dedicated to explain and elucidate AI systems. In order to accomplish such an explanation, XAI uses a variety of tools, devices and frameworks.
Panda, Swaroop, Roy, Shatarupa Thakurta
openaire   +1 more source

Explaining 'explaining away'

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993
'Explaining away' is a common pattern of reasoning in which the confirmation of one cause of an observed or believed event reduces the need to invoke alternative causes. The opposite of explaining away also an occur, where the confirmation of one cause increases belief in another. A general qualitative probabilistic analysis of intercausal reasoning is
M.P. Wellman, M. Henrion
openaire   +1 more source