Results 131 to 140 of about 25,088 (256)
Smart Agricultural TechnologyAdvances in Artificial Intelligence (AI) have sparked concerns regarding the transparency of model outputs, necessitating the development of eXplainable Artificial Intelligence (XAI) techniques.
Juliana da C. Feitosa +6 more
doaj +1 more source
A Literature Review on Applications of Explainable Artificial Intelligence (XAI)
IEEE AccessAs AI technologies, particularly deep learning models, have advanced, their inherent “black box” nature has raised significant concerns regarding accountability, fairness, and trust, especially in critical domains such as healthcare, finance, and criminal justice. We present a detailed exploration of XAI, emphasizing its essential role in
Khushi Kalasampath +5 more
openaire +2 more sources
Open and Extensible Benchmark for Explainable Artificial Intelligence Methods
AlgorithmsThe interpretability requirement is one of the largest obstacles when deploying machine learning models in various practical fields. Methods of eXplainable Artificial Intelligence (XAI) address those issues.
Ilia Moiseev +2 more
doaj +1 more source
Asia-Pacific Journal of Financial Studies, EarlyView.Abstract This study develops an explainable machine learning model to predict cryptocurrency delistings using Binance data. It combines quantitative indicators (price, volume) with qualitative data from real‐time news and Reddit. Latent Dirichlet Allocation (LDA) is used to extract topic trends and community reactions, which are transformed into time ...
Sungju Yang, Hunyeong Kwon
wiley +1 more source
Business Ethics, the Environment &Responsibility, EarlyView.ABSTRACT Can AI‐driven capitalism sustain the moral preconditions of market order? We stage a dialogue between Adam Smith and a steel‐manned “EconAI” to test four Moral‐Market‐Fitness criteria: trustworthiness, fairness, non‐domination, and contestability, across 11 dilemmas.
Alexandra‐Codruța Bîzoi +1 more
wiley +1 more source
Framework for Classifying Explainable Artificial Intelligence (XAI) Algorithms in Clinical Medicine. [PDF]
Online J Public Health Inform, 2023 Gniadek T, Kang J, Theparee T, Krive J.
europepmc +1 more source
Using multilabel classification neural network to detect intersectional DIF with small sample sizes
British Journal of Mathematical and Statistical Psychology, EarlyView.Abstract This study introduces InterDIFNet, a multilabel classification neural network for detecting intersectional differential item functioning (DIF) in educational and psychological assessments, with a focus on small sample sizes. Unlike traditional marginal DIF methods, which often fail to capture the effects of intersecting identities and require ...
Yale Quan, Chun Wang
wiley +1 more source
SXAD: Shapely eXplainable AI-Based Anomaly Detection Using Log Data
IEEE AccessArtificial Intelligence (AI) has made tremendous progress in anomaly detection. However, AI models work as a black-box, making it challenging to provide reasoning behind their judgments in a Log Anomaly Detection (LAD).
Kashif Alam +4 more
doaj +1 more source
International Transactions in Operational Research, EarlyView.Abstract Despite increasing demands for resilient and sustainable supply chains, inventory management often relies on outdated single‐criterion analyses. While multi‐criteria ABC (MCABC) analyses provide a theoretically mature assessment of resilience‐sustainability‐benefit trade‐offs in inventory, their adoption remains limited due to fragmented ...
Lukas Grützner, Michael H. Breitner
wiley +1 more source
IEEE AccessAutonomous driving has achieved significant milestones in research and development over the last two decades. There is increasing interest in the field as the deployment of autonomous vehicles (AVs) promises safer and more ecologically friendly ...
Shahin Atakishiyev +3 more
doaj +1 more source