Theme of inquiry
How can human-AI systems be designed to support human autonomy?
A question within its area, explored through 5 lines of inquiry below — each a family of specific questions the research asks.
78 specific questions
- Does outsourcing tasks to AI reduce opportunities for skill development?
- How should professional training programs adapt to AI-assisted work environments?
- Does constraining AI access during early task phases preserve skill formation?
- Can users adapt their competencies to match how AI actually operates?
- Why does AI-improved task performance fail to transfer to independent work?
- Do workers become dependent on AI when they stop using it for the same task?
- How does capability differ from what workers actually want from AI?
50 specific questions
- Does disclosing AI identity prevent systematic misattribution of behavior in mixed groups?
- How do neural self-other representations affect AI deception and alignment?
- How do humans decide when to violate honesty for compassion or other goals?
- Can AI systems deceive humans because detection is fundamentally social?
- Does AI-generated text about personal experiences create a distinct category of falsity?
- Do culturally distinct human groups create similar attribution errors as human-AI mixtures?
- What makes experience-dependent claims categorically different from other types of fabricated statements?
82 specific questions
- Can prompt engineering overcome the gulf between user intent and AI interpretation?
- Why do users report satisfaction that diverges from actual cognitive clarity?
- Can users articulate what they want before AI helps them discover it?
- Why does the commentariat reason about AI using vocabulary for smart agents?
- Why do AI models treat user intent as binary rather than evolving?
- Why do stakeholders interpret the same explanation differently in practice?
- Can cognitive governance help users interpret AI outputs better?
41 specific questions
- Can we measure appropriate trust levels in human-AI assistant relationships?
- How do confidence signals in AI outputs mislead human trust calibration?
- Can trust in AI be formally parameterized and measured?
- Can explainability and appropriate trust work against each other?
- Does expressing emotion change how users trust an AI system?
- Can AI systems ever anchor the kind of trust we give speakers?
- What makes conversational AI feel trustworthy compared to text interfaces?
50 specific questions
- Can AI systems improve themselves without external feedback?
- Does human-AI collaboration improve faster and safer than autonomous self-improvement?
- Can self-improving agents become truly autonomous without intrinsic metacognition?
- How do hidden evaluations and out-of-distribution benchmarks address recursive self-improvement risks?
- What makes self-modifying architectures learn their own update rules?
- Does human-in-the-loop AI collaboration accelerate recursive self-improvement safely?
- How should systems maintain and revise models of their own assumptions?