Results 51 to 60 of about 4,635,657 (309)

Can You Explain That? Lucid Explanations Help Human-AI Collaborative Image Retrieval

, 2019
While there have been many proposals on making AI algorithms explainable, few have attempted to evaluate the impact of AI-generated explanations on human performance in conducting human-AI collaborative tasks.
Burachas, Giedrius, Divakaran, Ajay, Kumar, Rakesh, Ray, Arijit, Yao, Yi   +4 more
core   +2 more sources

Logic for Explainable AI

2023 38th Annual ACM/IEEE Symposium on Logic in Computer Science (LICS), 2023
To appear in the 38th Annual ACM/IEEE Symposium on Logic in Computer Science (LICS ...
openaire   +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

Review of explainable artificial intelligence and its application prospect in earthquake science

地震科学进展
In the past decade, Artificial Intelligence (AI), as an important branch of computer science, has made breakthroughs in the research fields of computer vision, natural language processing, machine translation and so on.
Lihong Huang, Jian Li, Zhehan Liu, Xiaoming Wang, Jie Shang, Lei Gai, Hongmao Qiu, Ming Li, Ni Gong, Shoucheng Han, Yanyan Xu, Zeyu Liu   +11 more
doaj   +1 more source

A Multimodal Approach for the Risk Prediction of Intensive Care and Mortality in Patients with COVID-19

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, Konstantina D. Kourou, Costas Papaloukas, Vassiliki Triantafyllia, Vicky Lampropoulou, Eleni Siouti, Maria Papadaki, Maria Salagianni, Evangelia Koukaki, Nikoletta Rovina, Antonia Koutsoukou, Evangelos Andreakos, Dimitrios I. Fotiadis   +12 more
doaj   +1 more source

Explainable Software Bot Contributions: Case Study of Automated Bug Fixes

, 2019
In a software project, esp. in open-source, a contribution is a valuable piece of work made to the project: writing code, reporting bugs, translating, improving documentation, creating graphics, etc.
Monperrus, Martin
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

Visualizations for an Explainable Planning Agent

, 2018
In this paper, we report on the visualization capabilities of an Explainable AI Planning (XAIP) agent that can support human in the loop decision making.
Bellamy, Rachel K. E., Chakraborti, Tathagata, Dholakia, Mishal, Fadnis, Kshitij P., Kephart, Jeffrey O., Srivastava, Biplav, Talamadupula, Kartik   +6 more
core   +1 more source

The Grammar of Interactive Explanatory Model Analysis

, 2021
The growing need for in-depth analysis of predictive models leads to a series of new methods for explaining their local and global properties. Which of these methods is the best? It turns out that this is an ill-posed question.
Baniecki, Hubert, Biecek, Przemyslaw
core  

Advancing 6G: Survey for Explainable AI on Communications and Network Slicing

IEEE Open Journal of the Communications Society
The unprecedented advancement of Artificial Intelligence (AI) has positioned Explainable AI (XAI) as a critical enabler in addressing the complexities of next-generation wireless communications.
Haochen Sun, Yifan Liu, Ahmed Al-Tahmeesschi, Avishek Nag, Mohadeseh Soleimanpour, Berk Canberk, Hüseyin Arslan, Hamed Ahmadi   +7 more
semanticscholar   +1 more source