- Dubai launched a global system to label human vs. AI roles in
content creation. - The HMC Icons show how much humans or machines contributed, across writing, research, design, etc.
- Five main icons indicate levels of human–machine collaboration, from all-human to all-machine.
- Nine functional icons highlight specific stages AI was involved in (e.g., ideation, writing, visuals).
- It promotes transparency and encourages ethical, responsible use of AI worldwide
A Global First from Dubai
In a groundbreaking move that could reshape the way the world evaluates creative and scientific output, His Highness Sheikh Hamdan bin Mohammed bin Rashid Al Maktoum, Crown Prince of Dubai, Deputy Prime Minister, Minister of Defence, and Chairman of the Board of Trustees of the Dubai Future Foundation, has announced the launch of a first-of-its-kind global classification system. The initiative, called the Human–Machine Collaboration (HMC) Icons, aims to clearly differentiate between human and machine contributions in the research, production, and publication of creative, academic, scientific, and intellectual content. As reported by the Emirates News Agency (WAM), Sheikh Hamdan emphasized the urgency of this step in light of the rapidly evolving role of artificial intelligence (AI): “Distinguishing between human creativity and artificial intelligence has become a real challenge in light of today’s rapid technological advances. This calls for a new approach to recognise the growing role of intelligent machines. That’s why we launched the world’s first Human–Machine Collaboration Icons, a classification system that brings transparency to how research documents, publications, and content are created.” This new system is not just theoretical, it is being implemented immediately across Dubai’s government. Sheikh Hamdan has directed all Dubai Government entities to adopt the classification in their research and knowledge-based projects.
The HMC System: Breaking Down Human and Machine Roles
The HMC classification system, developed by the Dubai Future Foundation, offers a practical and visually accessible way for readers, researchers, and decision-makers to understand the extent of AI involvement in content creation. The classification introduces five core icons that indicate the level of collaboration between humans and intelligent machines:
- All Human – Content fully created by a human with no assistance from machines.
- Human Led – Human-created content that has been reviewed or improved by machines (e.g., grammar correction, fact-checking).
- Machine Assisted – A balanced collaboration where humans and machines worked iteratively to develop the content.
- Machine Led – Machines took the lead in content creation, with human oversight for quality and accuracy.
- All Machine – Content generated entirely by a machine, without any human input.
This approach is designed to inject clarity into a domain where AI use often goes undisclosed. In today’s digital landscape, where tools like generative AI, automation systems, and intelligent algorithms increasingly shape content, it’s often difficult for consumers or collaborators to understand how much of the work was created by a human or a machine. The term “intelligent machines” in this context covers a broad range of technologies: from AI and automation tools to robotics and algorithms, or any digital system that plays a role in the research, design, writing, analysis, or presentation of information.
Going Beyond Labels: Functional Icons for the Entire Process
The HMC system goes beyond top-level labels by introducing nine functional icons that show where, in the content creation process, human–machine collaboration took place. These icons are especially relevant to common research workflows and publication tasks, helping identify exactly how machines were involved.As a foundation, the classification system reflects key stages that typically involve machine assistance in research and content production. These include:
- Ideation
Generating and developing ideas, brainstorming, framing problems, and designing research approaches to create new insights or solutions. - Literature Review
Searching academic and non-academic sources to gather background knowledge that helps frame research questions and objectives. - Data Collection
Using various methods to gather information through primary (surveys, experiments) or secondary (existing datasets, archives) research. - Data Analysis
Applying qualitative and quantitative techniques to process and analyze the collected data for meaningful patterns and results. - Data Interpretation
Critically reflecting on analyzed data to uncover key findings, themes, and conclusions. - Writing
Expressing ideas, presenting research findings, and providing analysis through written language. - Translation
Converting text from one language to another while preserving the original meaning and intent. - Visuals
Creating images, charts, graphs, motion graphics, or other visual elements that help communicate information clearly. - Design
Organizing and formatting research outputs—such as reports, presentations, videos, or podcasts—to enhance clarity and engagement.
For instance, a research paper may be marked “Machine Assisted” with functional icons indicating AI helped in data analysis and visuals, but ideation and writing were entirely human-led. This enables a more nuanced and transparent evaluation, providing valuable insight not only into the what but the how of content creation. Importantly, the system does not attempt to assign numeric percentages or weights to the machine’s contribution, acknowledging that these judgments can often be subjective. Instead, it empowers creators to be honest and transparent about the involvement of machines, providing audiences with the information they need to evaluate authenticity and integrity.
A Call for Global Adoption
Sheikh Hamdan’s announcement is more than a local policy, it’s a global invitation. “We invite researchers, writers, publishers, designers, and content creators around the world to adopt this new global classification system and use it responsibly and in ways that benefit people,” he stated.This marks more than just a response to rapid technological change, it’s a clear signal that Dubai wants to shape how the world works with AI, not just adapt to it. As intelligent machines become more deeply involved in how we create and communicate, the line between human input and machine output continue to blur. The HMC Icons are meant to bring that line back into focus, not to hold innovation back, but to make sure people know what they’re looking at, and can trust what they’re reading or watching.By launching this system, Dubai is taking a leadership role in shaping ethical AI use and setting new standards for content transparency. The classification model is built to be adaptable across multiple sectors, such as academia, design, video production, and software development, where AI is increasingly integrated into everyday workflows. At its core, the system promotes honest disclosure of machine involvement, encouraging creators and institutions to uphold integrity by clearly showing how content is produced.By normalizing transparency, it may also reshape how AI involvement is perceived, less as a hidden shortcut and more as a declared, deliberate part of the creative and research process.
