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Argumentation and Logic Programming for Explainable and Ethical AI [PDF]
, 2020 In this paper we sketch a vision of explainability of intelligent systems as a logic approach suitable to be injected into and exploited by the system actors once integrated with sub-symbolic techniques.
Roberta Calegari +5 more
core +1 more source
The Shewanella oneidensis Fic enzyme SoFic targets the switch‐I region of EF‐Tu for AMPylation
FEBS Letters, EarlyView.Fic enzymes mediate diverse post‐translational modifications across all domains of life, including AMPylation. Prokaryotic EF‐Tu can be AMPylated and deAMPylated by the conserved Fic enzyme SoFic. Structural and biochemical approaches were used to characterize the effect of AMPylation on EF‐Tu, SoFic's enzymatic activities, and the enzyme‐target ...
Svenja Runge +6 more
wiley +1 more source
Explainable deep learning: concepts, methods, and new developments
, 2023 733Explainable AI (XAI) is an emerging research field bringing transparency to highly complex and opaque machine learning (ML) models. In recent years, various techniques have been proposed to explain and understand ML models, which have been previously ...
Wojciech Samek, Samek, Wojciech
core +1 more source
Increasing trust and fairness in machine learning applications within the mortgage industry
Machine Learning with Applications, 2022 The integration of machine learning in applications provides opportunities for increased efficiency in many organisations. However, the deployment of such systems is often hampered by the lack of insight into how their decisions are reached, resulting in
W. van Zetten, G.J. Ramackers, H.H. Hoos
doaj +1 more source
Smaller is better: nanobodies meet NMR
FEBS Letters, EarlyView.Nanobodies are single‐domain antigen‐binding fragments derived from camelid heavy chain antibodies. Their small size, high stability, and exceptional specificity make nanobodies uniquely useful probes for NMR studies of protein dynamics, transient conformational states, and protein–protein interactions.
Oleg Y. Dmitriev
wiley +1 more source
The effectiveness of explainable AI on human factors in trust models
Scientific ReportsExplainable AI has garnered significant traction in science communication research. Prior empirical studies have firmly established that explainable AI communication could improve trust in AI and that trust in AI engineers was argued to be an under ...
Justin C. Cheung, Shirley S. Ho
doaj +1 more source
Implications of causality in artificial intelligence
Frontiers in Artificial IntelligenceOver the last decade, investment in artificial intelligence (AI) has grown significantly, driven by technology companies and the demand for PhDs in AI. However, new challenges have emerged, such as the ‘black box’ and bias in AI models.
Luís Cavique
doaj +1 more source
Diagnostics, 2021 Background: Although several studies have been launched towards the prediction of risk factors for mortality and admission in the intensive care unit (ICU) in COVID-19, none of them focuses on the development of explainable AI models to define an ICU ...
Vasileios C. Pezoulas +12 more
doaj +1 more source
FEBS Letters, EarlyView.Transient oligomers formed by intrinsically disordered proteins may be ‘invisible’ to direct detection yet remain accessible to solution NMR through equilibrium‐exchange measurements and pressure‐jump experiments. Complementary methods report on mass, stoichiometry, selected distance distributions, morphology, and internal packing.
Martin D. Gelenter, Ad Bax
wiley +1 more source
Explainable & Safe Artificial Intelligence in Radiology
Journal of the Korean Society of RadiologyArtificial intelligence (AI) is transforming radiology with improved diagnostic accuracy and efficiency, but prediction uncertainty remains a critical challenge.
Synho Do
doaj +1 more source