Human Agency Scale Framework
Overview
A shared vocabulary for describing the spectrum between AI automation and human-AI collaboration. Unlike automation-focused frameworks (like SAE driving levels), HAS centers human agency as the organizing principle.
The Five Levels
H1: Full Automation
AI agent handles the task entirely on its own without human involvement.
Team dynamic: AI drives task completion
Example tasks: Transcribe data to worksheets, run monthly network reports
AI role: Replace human capabilities
H2: Minimal Human Input
AI agent needs human input at a few key points to achieve better task performance.
Team dynamic: AI drives with checkpoints
Example tasks: Devise trading strategies, accept payment on accounts
AI role: Replace with supervision
H3: Equal Partnership
AI agent and human work together to outperform either alone.
Team dynamic: True collaboration
Example tasks: Create game features including storylines, compile and analyze experimental data
AI role: Enhance human capabilities
H4: Human-Led with AI Support
AI agent needs human input to successfully complete the task.
Team dynamic: Human drives with AI assistance
Example tasks: Coordinate financial planning and budgeting, design training programs
AI role: Augment human work
H5: Essential Human Involvement
Task completion fully relies on human involvement.
Team dynamic: Human drives task completion
Example tasks: Participate in forums to stay current, direct client-facing activities
AI role: Support at margins
Why This Matters
Higher HAS levels aren’t inherently better, different levels suit different contexts. The framework helps:
- Developers design agents appropriate to the task (H1 tasks need autonomy; H3 tasks need coordination interfaces)
- Workers understand where their agency matters most
- Organizations set realistic expectations for AI deployment
- Researchers study human-AI collaboration systematically
Key Finding
45.2% of occupations have H3 as the dominant worker-desired level. This suggests workers broadly favor partnership over replacement, even for tasks that could be fully automated.
When to Use
- Designing AI agent interfaces and interaction patterns
- Setting expectations for AI adoption
- Identifying which tasks warrant autonomous vs. collaborative AI approaches
- Workforce planning and skill development
Limitations
- Self-reported preferences may not match revealed preferences
- Level assignment requires judgment calls
- Task boundaries aren’t always clean
- Worker exposure to AI capabilities varies widely
Related: 07-molecule—desire-capability-landscape, 07-atom—automation-vs-augmentation, 07-atom—worker-centered-ai-development-question