Results 251 to 260 of about 4,635,657 (309)

Explainable for Trustworthy AI

2023
Black-box Artificial Intelligence (AI) systems for automated decision making are often based on over (big) human data, map a user’s features into a class or a score without exposing why. This is problematic for the lack of transparency and possible biases inherited by the algorithms from human prejudices and collection artefacts hidden in the training ...
Fosca Giannotti, Francesca Naretto, Francesco Bodria   +2 more
openaire   +1 more source

Introduction to Explainable AI

Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, 2020
As Artificial Intelligence (AI) technologies are increasingly used to make important decisions and perform autonomous tasks, providing explanations that allow users to understand the AI has become a ubiquitous concern in human-AI interaction. Recently, a number of open-source toolkits are making the growing collection of of Explainable AI (XAI ...
Q. Vera Liao, Moninder Singh, Yunfeng Zhang, Rachel K. E. Bellamy   +3 more
openaire   +1 more source

Explainable AI in Industry

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019
Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching.
Krishna Gade, Sahin Cem Geyik, Krishnaram Kenthapadi, Varun Mithal, Ankur Taly   +4 more
openaire   +1 more source

Explainable AI in Healthcare

2020 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA), 2020
Artificial Intelligence (AI) is an enabling technology that when integrated into healthcare applications and smart wearable devices such as Fitbits etc. can predict the occurrence of health conditions in users by capturing and analysing their health data. The integration of AI and smart wearable devices has a range of potential applications in the area
Urja Pawar, Donna O'Shea, Susan Rea, Ruairi O'Reilly   +3 more
openaire   +1 more source

Designing Theory-Driven User-Centric Explainable AI

International Conference on Human Factors in Computing Systems, 2019
From healthcare to criminal justice, artificial intelligence (AI) is increasingly supporting high-consequence human decisions. This has spurred the field of explainable AI (XAI).
Danding Wang, Qian Yang, Ashraf Abdul, Brian Y. Lim   +3 more
semanticscholar   +1 more source

Explaining explainable AI

XRDS: Crossroads, The ACM Magazine for Students, 2019
How good are you at explaining your decisions? Are you better than a machine? Today, AI systems are being asked to explain their decisions. This article explores the challenges in solving this problem and approaches researchers are pursuing.
openaire   +1 more source

Explainable AI in Clinical Decision Support Systems: A Meta-Analysis of Methods, Applications, and Usability Challenges

Healthcare
Background: Theintegration of artificial intelligence (AI) into clinical decision support systems (CDSSs) has significantly enhanced diagnostic precision, risk stratification, and treatment planning.
Qaiser Abbas, Woonyoung Jeong, S. Lee
semanticscholar   +1 more source

Explainable AI for lung cancer detection via a custom CNN on CT images

Scientific Reports
Lung cancer, which claims 1.8 million lives annually, is still one of the leading causes of cancer-related deaths globally. Patients with lung cancer frequently have a bad prognosis because of late-stage detection, which severely limits treatment options
Mohamed Hammad, M. El-Affendi, A. El-latif, A. A. Ateya, Gauhar Ali, Paweł Pławiak   +5 more
semanticscholar   +1 more source

Secure and Transparent Banking: Explainable AI-Driven Federated Learning Model for Financial Fraud Detection

Journal of Risk and Financial Management
The increasing sophistication of fraud has rendered rule-based fraud detection obsolete, exposing banks to greater financial risk, reputational damage, and regulatory penalties.
Saif Khalifa Aljunaid, Saif Almheiri, Hussain Dawood, Muhammad Adnan Khan   +3 more
semanticscholar   +1 more source

LLMs for Explainable AI: A Comprehensive Survey

arXiv.org
Large Language Models (LLMs) offer a promising approach to enhancing Explainable AI (XAI) by transforming complex machine learning outputs into easy-to-understand narratives, making model predictions more accessible to users, and helping bridge the gap ...
Ahsan Bilal, David S. Ebert, Beiyu Lin
semanticscholar   +1 more source