Results 11 to 20 of about 73,912 (246)

XAI—Explainable artificial intelligence [PDF]

Science Robotics, 2019
Explainability is essential for users to effectively understand, trust, and manage powerful artificial intelligence applications.
David Gunning, Mark Stefik, Jaesik Choi, Timothy Miller, Simone Stumpf, Guang-Zhong Yang   +5 more
openaire   +6 more sources

Explainable artificial intelligence

American Journal of Orthodontics and Dentofacial Orthopedics
Axel-Jan Rousseau, Melvin Geubbelmans, Dirk Valkenborg, Tomasz Burzykowski   +3 more
  +16 more sources

Explainable & Safe Artificial Intelligence in Radiology

Journal of the Korean Society of Radiology
Artificial intelligence (AI) is transforming radiology with improved diagnostic accuracy and efficiency, but prediction uncertainty remains a critical challenge.
Synho Do
doaj   +3 more sources

Editorial: Explainable artificial intelligence

Frontiers in Computer Science, 2023
Chathurika S. Wickramasinghe, Daniel Marino, Kasun Amarasinghe   +2 more
doaj   +2 more sources

Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps [PDF]

, 2020
With advances in reinforcement learning (RL), agents are now being developed in high-stakes application domains such as healthcare and transportation.
Amir, Ofra, André, Elisabeth, Huber, Tobias, Weitz, Katharina   +3 more
core   +2 more sources

The Pragmatic Turn in Explainable Artificial Intelligence (XAI) [PDF]

, 2019
In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI.
Páez, Andrés
core   +3 more sources

Representing First-Order Causal Theories by Logic Programs [PDF]

, 2011
Nonmonotonic causal logic, introduced by Norman McCain and Hudson Turner, became a basis for the semantics of several expressive action languages. McCain's embedding of definite propositional causal theories into logic programming paved the way to the ...
Armando, FANGKAI YANG, Ferraris, Gelfond, Giunchiglia, Glivenko, JOOHYUNG LEE, Lee, Lifschitz, Lifschitz, Lifschitz, Lifschitz, Lifschitz, McCain, Mints, PAOLO FERRARIS, Shanahan, VLADIMIR LIFSCHITZ, YULIYA LIERLER   +18 more
core   +7 more sources

A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems [PDF]

, 2019
We propose a set of compositional design patterns to describe a large variety of systems that combine statistical techniques from machine learning with symbolic techniques from knowledge representation.
Teije, Annette ten, van Harmelen, Frank
core   +2 more sources

A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation

, 2020
Traditionally, abnormal heart sound classification is framed as a three-stage process. The first stage involves segmenting the phonocardiogram to detect fundamental heart sounds; after which features are extracted and classification is performed.
Denman, Simon, Dissanayake, Theekshana, Fernando, Tharindu, Fookes, Clinton, Ghaemmaghami, Houman, Sridharan, Sridha   +5 more
core   +1 more source

Context-Aware Negative Sampling for Sequential Recommendation

IEEE Access
Recommender systems have become essential in large-scale e-commerce and content platforms. While user preferences are crucial in generating recommendations, the context in which recommendations are made—such as time, location, and occasion— ...
Jinseok Seol, Jaesik Choi
doaj   +1 more source