Results 21 to 30 of about 29,455 (191)

conrad-blucher-institute/xai-raster-vis-tools: Release for AMS 2023

, 2022
Scripts for visualizing (rows, columns, channels) outputs of XAI ...
Evan Krell
core   +1 more source

ayiork/Label-Free-XAI: Reproducibility Study: Label-Free Explainability for Unsupervised Models

, 2023
This repository contains the reproducibility study of Label-Free XAI, a new framework to adapt explanation methods to unsupervised models. For more details, please read our ICML 2022 paper: 'Label-Free Explainability for Unsupervised Models'
Mara Pislar, Andreas Giorkatzi, Jonathan Crabbé, Ana-Ivaschescu, Bogdan Cebere   +4 more
core   +1 more source

Improving Trust via XAI and Pre-Processing for Machine Learning of Complex Biomedical Datasets

Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022
Complex datasets hold a special place among engineers as the engineering community seeks to solve some of the world’s most difficult problems, but with complexity, comes difficulty in analysis and interpretation.
Brandon Hines, Douglas Talbert, Steven Anton   +2 more
doaj   +1 more source

eXplainable AI (XAI)

SIGGRAPH Asia 2020 Courses, 2020
• Do Machine Learning algorithms have a Soul? • Could they understand every day's reality as us Humans do? • What the consequence of their Creativity? • Can they help us to understand world better?
Hughes, R, Edmond, C, Wells, L, Glencross, M, Zhu, L, Bednarz, T   +5 more
openaire   +3 more sources

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 0001, Simone Stumpf, Guang-Zhong Yang   +5 more
openaire   +6 more sources

COMPARATIVE STUDY OF XAI USING FORMAL CONCEPT LATTICE AND LIME

ICTACT Journal on Soft Computing, 2022
Local Interpretable Model Agnostic Explanation (LIME) is a technique to explain a black box machine learning model using a surrogate model approach.
Bhaskaran Venkatsubramaniam, Pallav Kumar Baruah   +1 more
doaj   +1 more source

SeizFt: Interpretable Machine Learning for Seizure Detection Using Wearables

Bioengineering, 2023
This work presents SeizFt—a novel seizure detection framework that utilizes machine learning to automatically detect seizures using wearable SensorDot EEG data.
Irfan Al-Hussaini, Cassie S. Mitchell
doaj   +1 more source

Explainable Artificial Intelligence in Healthcare: XAI-Healthcare 2021 [PDF]

, 2021
XAI-Healthcare event provided a place for intensive discussion on all aspects of eXplainable Arti cial Intelligence (XAI) in the medical and healthcare  eld.
Juarez, Jose M., Stiglic, Gregor, Zhengxing, Huang, Verbert, Katrien   +3 more
core  

Causal Explanations and XAI

CoRR, 2022
To appear in Causal Learning and Reasoning ...
openaire   +4 more sources

Transparency and Explainable AI: Bridging Privacy, Fairness, and Accountability [PDF]

Revista de Management Comparat International
This paper examines whether Explainable Artificial Intelligence (XAI) can address AI ethics, specifically, privacy, fairness, and accountability, by linking technical transparency with interpretability to facilitate ethical oversight.
Dragos VIERU, Renée-Maria SCHMITT
doaj   +1 more source