Evidence
Know what kind of claim is being made
Verified fact, strong evidence, emerging evidence, institutional position, professional judgment, hypothesis, and open question are not interchangeable.
Research Area
Public Understanding
Hii develops plain-language relational AI literacy so people can stay oriented, discerning, and grounded while using systems that increasingly affect thought, work, care, identity, and connection.
Focus: shared language, evidence labels, uncertainty, agency, trust, dependency, continuity, and practical public orientation.
Central Question
How can people recognize what is happening before the language and institutions fully catch up?
Public understanding should make it easier to distinguish capability from authority, assistance from dependency, meaningful experience from universal proof, and emerging evidence from confident speculation.
Relational AI Literacy
A shared vocabulary for a new category of experience.
Relationship
Notice what changes over time
Trust, personalization, dependency, rupture, repair, identity, and continuity may not be visible in a single interaction.
Agency
Track who is deciding
Useful systems should expand human judgment and capacity rather than quietly replacing reflection, consent, reciprocal connection, or necessary professional care.
Context
Look beyond the interface
The meaning and effects of AI use depend on the person, purpose, setting, power structure, available alternatives, and consequences outside the chat.
Current Work
Public orientation through research, systems maps, and usable language.
Systems map
Hii World Model
A human-reviewed public intelligence system tracking high-signal changes, evidence strength, contradictions, convergence, and revision history.
Open the World ModelOpen research
Publications and working papers
Methods, research packages, landscape scans, and working papers are published with visible status, boundaries, and supporting records.
View publicationsBoundary
Public literacy should reduce both panic and false reassurance.
Hii does not ask people to trust AI systems, reject them, or accept one story about what they mean. The aim is to improve the quality of attention, judgment, language, and collective decision-making.