Results 31 to 40 of about 220,602 (307)

What Is the Role of Explainability in Medical Artificial Intelligence? A Case-Based Approach

Bioengineering
This article reflects on explainability in the context of medical artificial intelligence (AI) applications, focusing on AI-based clinical decision support systems (CDSS).
Elisabeth Hildt
doaj   +1 more source

Explaining Explaining

Proceedings of the 1st International Conference on Explainable AI for Neural and Symbolic Methods
Explanation is key to people having confidence in high-stakes AI systems. However, machine-learning-based systems -- which account for almost all current AI -- can't explain because they are usually black boxes. The explainable AI (XAI) movement hedges this problem by redefining "explanation".
Nirenburg, Sergei, McShane, Marjorie, Goodman, Kenneth W., Oruganti, Sanjay   +3 more
openaire   +2 more sources

Enhancing Decision Tree based Interpretation of Deep Neural Networks through L1-Orthogonal Regularization

, 2019
One obstacle that so far prevents the introduction of machine learning models primarily in critical areas is the lack of explainability. In this work, a practicable approach of gaining explainability of deep artificial neural networks (NN) using an ...
Huber, Marco F., Maucher, Johannes, Schaaf, Nina   +2 more
core   +1 more source

X-OODM: Explainable Object-Oriented Design Methodology

IEEE Access
In software applications and decision-making systems, the explainability features can be instrumental for explicating internal working, accountability, understanding, fairness, and interpretation of decisions, processes, and data.
Abqa Javed, Muhammad Shoaib, Abdul Jaleel   +2 more
doaj   +1 more source

Explainable Machine Learning in Human Gait Analysis: A Study on Children With Cerebral Palsy

IEEE Access, 2023
This work investigates the effectiveness of various machine learning (ML) methods in classifying human gait patterns associated with cerebral palsy (CP) and examines the clinical relevance of the learned features using explainability approaches.
Djordje Slijepcevic, Matthias Zeppelzauer, Fabian Unglaube, Andreas Kranzl, Christian Breiteneder, Brian Horsak   +5 more
doaj   +1 more source

Explaining Latent Factor Models for Recommendation with Influence Functions

, 2018
Latent factor models (LFMs) such as matrix factorization achieve the state-of-the-art performance among various Collaborative Filtering (CF) approaches for recommendation.
Bilgic Mustafa, He Xiangnan, Kingma Diederik, Koh Pang Wei, Lee Daniel D., Martens James, Rendle Steffen, Zhang Yongfeng   +7 more
core   +1 more source

Explaining Young Mortality [PDF]

SSRN Electronic Journal, 2011
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
O’Hare, Colin, Li, Youwei, O'Hare, Colin   +2 more
openaire   +3 more sources

Stressful Events Reported by Childhood Cancer Survivors and Community Controls From the St. Jude Lifetime (SJLIFE) Cohort: A Mixed Method Study

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Introduction Characterizing stressful events reported by childhood cancer survivors experienced throughout the lifespan may help improve trauma‐informed care relevant to the survivor experience. Methods Participants included 2552 survivors (54% female; 34 years of age) and 469 community controls (62% female; 33 years of age) from the St.
Megan E. Ware, Colter K. Clayton, Fang Wang, Sedigheh Mirzaei, Kyla Shelton, Tara M. Brinkman, Victoria Willard, Katianne M. Howard Sharp, Kevin Krull, Lindsay F. Schwartz, Melissa M. Hudson, Gregory T. Armstrong, Kirsten K. Ness, Rachel T. Webster   +13 more
wiley   +1 more source

Dot-to-Dot: Explainable Hierarchical Reinforcement Learning for Robotic Manipulation

, 2019
Robotic systems are ever more capable of automation and fulfilment of complex tasks, particularly with reliance on recent advances in intelligent systems, deep learning and artificial intelligence.
Beyret, Benjamin, Faisal, A. Aldo, Shafti, Ali   +2 more
core   +1 more source

Criterion‐Related Validity of the Neuropsychological Quick Assessment for Screening Cognitive, Motor, and Behavioral Impairments in Patients With Pediatric Brain Tumors: An Observational Pilot Study

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Neuropsychological complications may impair the qualitative prognosis of patients with pediatric brain tumors. However, multifaceted evaluations cannot be conducted in all patients because they are time consuming and burdensome for patients.
Ami Tabata, Keita Ueda, Tsukasa Ueno, Katsutsugu Umeda, Kentaro Ueno, Kanade Tanaka, Noyuri Nishida, Rie Yamawaki, Megumi Nukui, Yoshiki Arakawa   +9 more
wiley   +1 more source