Results 141 to 150 of about 20,583 (298)

A Statistical‐Process Hybridized Approach to Modeling Permafrost Distribution in a Boreal Wetland Ecosystem, Whatì, NT, Canada

Permafrost and Periglacial Processes, EarlyView.
ABSTRACT High‐resolution mapping of permafrost in ecologically and topographically complex landscapes remains a major challenge. Existing models of permafrost extent often rely on equilibrium assumptions, which can misrepresent conditions in regions where permafrost persists largely due to ecosystem structure.
Philip P. Bonnaventure, Sophie MacLean, Nick C. Noad, Madeleine C. Garibaldi   +3 more
wiley   +1 more source

XAI: On Explainability and the Obligation to Explain

Digital Society
Abstract The increasing relevance of AI systems paired with their repeatedly observed opacity gave rise to the field of explainable artificial intelligence (XAI). Methods of XAI are being developed and evaluated based on whether they overcome said opacity by providing explanations, thereby apparently pursuing an ...
Karoline Reinhardt, Oliver Buchholz
openaire   +1 more source

How AI and Digital Technologies Can Enhance Sustainable Livestock Manure Management: An Overview From Treatment to Distribution

Sustainable Development, EarlyView.
ABSTRACT Sustainable livestock manure management sits at the nexus of climate, nutrient circularity and water quality. This review explores how artificial intelligence (AI) and digital platforms are used across four management stages, that is, treatment, storage, valorisation and distribution, and figures out where integration fails to deliver ...
Zhan Shi, Feihong Liang, Francesco Marinello, Andrea Pezzuolo   +3 more
wiley   +1 more source

/xai/xai School; Excellent Group Shot (Majority Ju Students) 3

, 1980
/xai/xai ...
Lee, Richard B.
core  

From Prediction to Prevention: An Explainable GeoAI Framework for Flood Susceptibility and Urban Exposure Assessment Using Machine and Deep Learning Models

Sustainable Development, EarlyView.
ABSTRACT Rapid urbanisation and intensifying rainfall have increased cities' vulnerability to flooding, posing major challenges to sustainable development. Although machine learning models have improved flood prediction accuracy, most remain limited by their black‐box nature and lack of actionable insights.
Abdulwaheed Tella, Izni Zahidi, Chow Ming Fai, Chukwuma Okolie, Quoc Bao Pham   +4 more
wiley   +1 more source

Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma

Nature Communications
Artificial intelligence (AI) systems have been shown to help dermatologists diagnose melanoma more accurately, however they lack transparency, hindering user acceptance.
Tirtha Chanda, Katja Hauser, Sarah Hobelsberger, Tabea-Clara Bucher, Carina Nogueira Garcia, Christoph Wies, Harald Kittler, Philipp Tschandl, Cristian Navarrete-Dechent, Sebastian Podlipnik, Emmanouil Chousakos, Iva Crnaric, Jovana Majstorovic, Linda Alhajwan, Tanya Foreman, Sandra Peternel, Sergei Sarap, İrem Özdemir, Raymond L. Barnhill, Mar Llamas-Velasco, Gabriela Poch, Sören Korsing, Wiebke Sondermann, Frank Friedrich Gellrich, Markus V. Heppt, Michael Erdmann, Sebastian Haferkamp, Konstantin Drexler, Matthias Goebeler, Bastian Schilling, Jochen S. Utikal, Kamran Ghoreschi, Stefan Fröhling, Eva Krieghoff-Henning, Reader Study Consortium, Titus J. Brinker   +35 more
doaj   +1 more source

AI‐Driven Circular Construction Waste Management for Advancing Sustainable Development

Sustainable Development, EarlyView.
ABSTRACT Construction and demolition waste (C&DW) represents up to 40% of global solid waste, posing a significant barrier to achieving circular economy (CE) objectives and the Sustainable Development Goals (SDGs), particularly SDG 11 and SDG 12. However, construction waste management (CWM) systems remain constrained by fragmented data environments ...
Mohamed T. Elnabwy, Pablo Martinez
wiley   +1 more source

Explainable machine learning for breast cancer diagnosis from mammography and ultrasound images: a systematic review

BMJ Health & Care Informatics
Background Breast cancer is the most common disease in women. Recently, explainable artificial intelligence (XAI) approaches have been dedicated to investigate breast cancer. An overwhelming study has been done on XAI for breast cancer.
Worku Jimma, Daraje kaba Gurmessa
doaj   +1 more source

Generative AI—the Transgression of Technology

Systems Research and Behavioral Science, EarlyView.
ABSTRACT This article offers a systems‐theoretical analysis of generative artificial intelligence (GenAI) grounded in Niklas Luhmann's sociology of technology. It addresses a central conceptual problem: How GenAI can be understood within a theoretical framework that has traditionally defined technology as a means of stabilising action through causal ...
Jesper Tække
wiley   +1 more source

Externalist XAI?

Developers of artificial intelligence (AI) often cannot explain the inferences their neural networks make, at least not in ways that satisfy user needs.
Søgaard, Anders
core   +1 more source