The Artificial Intelligence, Machine Learning, and Data Science Journal publishes research advancing the theory, methods, and applications of intelligent systems. Our scope spans machine learning and deep learning, artificial intelligence, neural networks, and the data science that turns large and complex datasets into insight.

We welcome contributions on learning algorithms and architectures, natural language processing, computer vision, reinforcement and generative models, big-data analytics, and the deployment of AI in science, industry, and society. Equally important to us is responsible AI — work on fairness, interpretability, robustness, privacy, and the ethical and societal implications of intelligent systems.

Submissions are evaluated for technical novelty, methodological soundness, and the clarity and reproducibility of their results; we encourage authors to share code and data where possible. Through open access the journal aims to accelerate progress across the AI and data-science community, serving researchers and practitioners who develop, study, and apply these rapidly evolving technologies.

We publish methodological research, novel applications, benchmark and empirical studies, and critical reviews, including work on the responsible and ethical use of AI. Authors are encouraged to share code and data so that results can be reproduced and built upon. Available open access, the journal serves researchers and practitioners across academia and industry who develop, evaluate, and deploy intelligent systems in science and society.

Topics Covered

• Machine Learning & Deep Learning
• Artificial Intelligence
• Neural Networks
• Natural Language Processing
• Computer Vision
• Big Data Analytics
• Reinforcement & Generative Models
• AI Ethics & Trustworthy AI