Theme of inquiry
How should we integrate AI while protecting human capability?
A question within its area, explored through 7 lines of inquiry below — each a family of specific questions the research asks.
68 specific questions
- Can humans build reliable oversight for increasingly complex AI systems?
- Can humans remain meaningfully in the loop as AI autonomy scales?
- Does keeping humans in the loop protect against AI risk without scrutiny capacity?
- Can targeted human oversight work better than full autonomy or micromanagement?
- How does treating AI as an agent affect user autonomy and decision-making?
- Why does constant human oversight degrade agent coherence and induce rubber-stamping?
- Does human-AI collaboration improve faster and safer than autonomous self-improvement?
74 specific questions
- Why do stakeholders interpret the same explanation differently in practice?
- Can AI output be genuinely novel or only at the margins?
- What separates performative behavioral change from actual capability development in AI?
- Why can't AI models internalize audiences the way human experts do?
- How should designers make invisible AI state legible to users?
- Why does mimicking human behavior differ from simulating human cognition?
- How does AI knowledge become structurally different from written sources?
31 specific questions
- Does deploying AI uniformly across task types increase or decrease workplace inequality?
- How does concentration of AI capability across firms affect labor market outcomes?
- How does concentrated AI exposure across workers affect firm-level employment demand?
- Does AI adoption rise or fall as worker education and wages increase?
- Which firms capture the cost advantages from labor-to-AI substitution?
- How do worker-side adaptation effects interact with firm-level substitution patterns?
- Does codifying expertise into AI agents drive faster labor substitution?
49 specific questions
- What social norms do AI systems consistently fail to understand?
- How do language models predict collective social norms better than individual humans?
- Do different AI models independently converge on the same social outputs?
- Do AI systems need embodiment to understand social norms?
- Can AI predict social norms well enough without embodied experience?
- Why do behavioral outcomes alone mislead claims about social mechanisms?
- Should safety constraints trade off against representing authentic human value diversity?
65 specific questions
- What task characteristics determine whether humans or agents should handle work?
- Can worker preference serve as a legitimate axis for delegation design?
- Why do some occupations need human-AI partnership more than others?
- What distinguishes perception contribution from decision authority in collaboration?
- Why do people treat AI systems as group members rather than just tools?
- What makes a task suitable for equal partnership instead of automation?
- What task characteristics determine whether delegation can succeed?
51 specific questions
- When should an AI system actively intervene versus remain silent?
- How do active-participant AI systems risk being perceived as intrusive or inappropriate?
- What social boundaries must proactive agents respect during conversation?
- What distinguishes over-intervention from useful proactive AI assistance?
- Can AI distinguish when validation helps versus when confrontation is needed?
- Can timing and context awareness reduce the cognitive cost of AI suggestions?
- Can proactive AI agents deploy politeness strategies without appearing intrusive?
56 specific questions
- Does AI assistance help people learn skills or just delegate the task?
- Does AI assistance transfer learning gains to independent tasks without scaffolding?
- Does constraining AI access during early task phases preserve skill formation?
- Does AI-assisted performance transfer to independent task completion?
- Does outsourcing tasks to AI reduce opportunities for skill development?
- Why does AI-improved task performance fail to transfer to independent work?
- When students use AI feedback, which cognitive tasks must they keep doing?