Editorial Policy
Editorial Policy
AI Discovery Hub publishes practical, educational material about artificial intelligence and software engineering. This page describes the standards used to keep that material useful and trustworthy.
Originality and attribution
Articles should add original explanation, implementation detail, analysis, or experimental evidence. Ideas, quotations, datasets, images, and code from others must be attributed and used under appropriate terms. When an article is also distributed on another platform, AI Discovery Hub should be identified as the primary version through a canonical link where the platform supports it.
Research and technical review
Primary sources—official documentation, specifications, repositories, and research papers—are preferred for technical claims. Code examples should be internally reviewed and written so that readers can identify dependencies, assumptions, and security limitations. Claims based on estimates or hypothetical examples are labeled accordingly.
Use of AI tools
AI tools may assist with outlining, editing, code review, or illustration. They are not treated as authoritative sources. A human reviews the final article for relevance, accuracy, attribution, and clarity before publication. Automatically generated or imported material is not published without meaningful human curation.
Sponsorships and conflicts
Paid relationships, affiliate links, free products, and other material conflicts of interest will be disclosed near the affected content. Sponsors do not receive a guarantee of positive coverage.
Corrections
Errors are corrected in the article when verified. Significant corrections should explain what changed. Readers can report issues through the Contact page.
Comments
Post a Comment
Thank you for visiting AI Hub Discovery! We welcome thoughtful comments, questions, and discussions about AI, machine learning, software engineering, and cloud technologies. Please keep comments respectful, relevant, and free of spam or promotional links.