Journal of Artificial Intelligence
A Gold Open Access journal advancing the boundaries of intelligent systems, neural networks, and responsible artificial intelligence research.
About the Journal
Rigorous peer-reviewed research in artificial intelligence, machine learning, deep learning, computer vision, and neural networks.
Scholarly Open publishes highly relevant and FAIR-aligned research across our journals. We focus on transparent processes, rapid dissemination, and strong author support.
Scope & Coverage
- •Artificial Intelligence & Machine Learning
- •Neural Networks & Deep Learning
- •Natural Language Processing
- •Computer Vision
- •AI Ethics & Responsible AI
- •Robotics & Automation
- •Knowledge Representation
- •AI Applications & Systems
- •Quantum Computing
- •Human-AI Interaction
Journal Sections
Editorial Board
Our distinguished editorial board members are leading experts in their fields, overseeing our rigorous peer review and ensuring high publication standards.
Editor-in-Chief
Prof. Dr. Geoffrey Hinton
Vector Institute, Canada
Associate Editors
Prof. Dr. Yann LeCun
Meta AI, USA
Prof. Dr. Yoshua Bengio
University of Montreal, Canada
Prof. Dr. Fei-Fei Li
Stanford University, USA
Editorial Board Members
Prof. Dr. Stuart Russell
UC Berkeley, USA
Prof. Dr. Demis Hassabis
DeepMind, UK
Dr. Kate Crawford
Microsoft Research, USA
Prof. Dr. Michael Jordan
UC Berkeley, USA
Prof. Dr. Dario Amodei
Anthropic, USA
Dr. Timnit Gebru
DAIR, USA
Join Our Editorial Team
We are always looking for distinguished scholars to join our editorial board. If you are interested in contributing to the advancement of open access, we would love to hear from you.
Articles
Explore the latest high-impact peer-reviewed research articles published open-access in this journal.
Sample Articles
These article outlines are illustrative examples for our launch journals.
This study provides an in-depth analysis of attention mechanism variations in transformer architectures. We examine computational efficiency, interpretability, and performance trade-offs across multiple attention types, providing guidelines for practitioners.
We present a comprehensive framework for addressing ethical concerns in LLM deployment, covering bias mitigation, transparency, accountability, and fairness. Our framework integrates technical and governance approaches with evidence from real-world implementations.
This systematic review synthesizes findings from 156 studies on deep learning applications in medical imaging. We identify key algorithms, datasets, and challenges while proposing standardized evaluation metrics for clinical deployment.
Submit to Journal of Artificial Intelligence
Please review our author guidelines and prepare your manuscript according to our submission requirements. We welcome original research, reviews, and methodological contributions that support FAIR scholarship.