What collaboration level do workers actually want with AI?
Explores whether workers prefer full automation, equal partnership, or continuous human control across different tasks. Understanding worker preferences could reshape how organizations deploy AI systems.
The WORKBank framework moves beyond "can AI do this task?" to "how much human involvement do workers want?" by surveying 1,500 domain workers and AI experts across 844 tasks spanning 104 occupations. The HumanAgency Scale (HAS) defines five levels:
- H1: AI handles the task entirely on its own
- H2: AI needs minimal human input for optimal performance
- H3: AI and human form equal partnership, outperforming either alone
- H4: AI requires human input to successfully complete the task
- H5: AI cannot function without continuous human involvement
The key finding: H3 (equal partnership) emerges as the dominant worker-desired level in 45.2% of occupations (47 out of 104). Workers don't want full automation for most tasks — they want collaboration. This challenges the automation-or-not framing that dominates AI deployment discourse.
Crossing worker desire against AI capability produces four deployment zones: Green Light (workers want automation, AI can deliver), Red Light (workers don't want it, even though AI could), R&D Opportunity (workers want it, AI can't yet), Low Priority (neither workers want it nor AI can deliver). The finding that 41% of Y Combinator startup investments are concentrated in Red Light and Low Priority zones — targeting automation where workers resist it or capability where demand is low — suggests systematic misalignment between AI investment and human preference.
Unlike SAE driving automation levels that adopt an "AI-first" perspective, HAS provides a human-centered lens. "Higher HAS levels are not inherently better — different levels suit different AI roles. Tasks at H1-H2 favor automation approaches, while H3-H5 tasks benefit from augmentation strategies." This connects to Does machine agency exist on a spectrum rather than binary? as a demand-side complement: HAII describes what AI can do; HAS describes what workers want.
The skill shift data is equally significant: "traditionally high-wage skills like analyzing information are becoming less emphasized, while interpersonal and organizational skills are gaining more importance." The top 10 skills requiring highest human agency span interpersonal, organizational, decision-making, and quality judgment — not information processing. Since What makes delegation work beyond just splitting tasks?, HAS adds a twelfth axis: worker preference for human involvement, which may diverge from both capability and optimality.
Inquiring lines that use this note as a source 9
This note is a source for these synthesized inquiries. Follow a line forward into its question, or open it to trace back to all of its sources.
- Which workplace tasks see productivity gains when AI and users align?
- Why do some occupations need human-AI partnership more than others?
- Can workers reallocate to subjective tasks that resist automation indefinitely?
- What economic role remains for human labor after bottleneck automation?
- Why do 45 percent of workers want equal partnership with AI rather than full automation?
- What tasks do users actually want AI to handle versus what can it automate?
- What makes a task suitable for equal partnership instead of automation?
- How does capability differ from what workers actually want from AI?
- Can worker preference serve as a legitimate axis for delegation design?
Related concepts in this collection 4
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Does machine agency exist on a spectrum rather than binary?
Rather than viewing AI as either autonomous or controlled, does machine agency actually operate across five distinct levels from passive to cooperative? Understanding this spectrum matters because it shapes how users calibrate trust and control expectations.
HAS is the worker-desire complement to HAII's capability spectrum
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What makes delegation work beyond just splitting tasks?
Delegation is more than task decomposition. What dimensions of a task—like verifiability, reversibility, and subjectivity—determine whether an agent can safely and effectively handle it?
HAS adds worker preference as a delegation axis
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When should human-agent systems ask for human help?
Explores the timing problem in collaborative AI systems: since there's no objective metric for optimal interruption, how can we design deferral mechanisms that know when to involve humans without constant disruption or silent failures?
Magentic-UI's six mechanisms operationalize H3-H5 level interactions
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Does incremental AI replacement erode human influence over society?
Explores whether gradual AI adoption—without dramatic breakthroughs—can silently degrade human agency by removing the labor that kept institutions implicitly aligned with human needs.
skill shift from information-processing to interpersonal quantifies the disempowerment trajectory
Related papers in this collection 8
Papers most semantically related to this note, ranked by cosine similarity in the embedding space.
- Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforce
- Humans learn to prefer trustworthy AI over human partners
- TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks
- Position: Towards Bidirectional Human-AI Alignment
- Working with AI: Measuring the Occupational Implications of Generative AI
- Beyond Preferences in AI Alignment
- Trust in Human-AI Interaction: Scoping Out Models, Measures, and Methods
- Quantifying Human-AI Synergy
Original note title
workers desire equal human-AI partnership for 45 percent of occupations — the HumanAgency Scale maps five collaboration levels across 844 tasks revealing four deployment zones