Results 21 to 30 of about 60,361 (257)

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 Simulations Through Self Explaining Agents [PDF]

Journal of Artificial Societies and Social Simulation, 2010
Several strategies are used to explain emergent interaction patterns in agent-based simulations. A distinction can be made between simulations in which the agents just behave in a reactive way, and simulations involving agents with also pro-active (goal-directed) behavior.
Maaike Harbers, John-Jules Meyer, Karel van den Bosch   +2 more
openaire   +3 more sources

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

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

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

CEPS Task Force on Artificial Intelligence and Cybersecurity Technology, Governance and Policy Challenges Task Force Evaluation of the HLEG Trustworthy AI Assessment List (Pilot Version). CEPS Task Force Report 22 January 2020 [PDF]

, 2020
The Centre for European Policy Studies launched a Task Force on Artificial Intelligence (AI) and Cybersecurity in September 2019. The goal of this Task Force is to bring attention to the market, technical, ethical and governance challenges posed by the ...
Fantin, Stephano, Ferreira, Afonso, Pupillo, Lorenzo   +2 more
core  

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

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

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

Psychosocial Outcomes in Patients With Endocrine Tumor Syndromes: A Systematic Review

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Introduction The combination of disease manifestations, the familial burden, and varying penetrance of endocrine tumor syndromes (ETSs) is unique. This review aimed to portray and summarize available data on psychosocial outcomes in patients with ETSs and explore gaps and opportunities for future research and care.
Daniël Zwerus, Floortje Strobbe, Annemarie A. Verrijn Stuart, Hanneke M. van Santen, Gerlof D. Valk, Sasja A. Schepers, Rachel S. van Leeuwaarde   +6 more
wiley   +1 more source