Before the Threshold
A white paper examining search behavior as an early signal of emotional distress before formal disclosure or clinical recognition.
Read the white paperIndependent research initiative
The Uplift Index
A research initiative studying how aggregated search behavior can reveal population-level signals of human well-being, social disconnection, and emerging societal needs before they become visible to traditional institutions.
Research focus: population-level emotional distress, social disconnection, financial stress, and emerging societal needs.
Short thesis
Before the Threshold
Many forms of emotional distress are not immediately disclosed to doctors, surveys, employers, family members, or public institutions. Before someone says "I am lonely," "I need help," or "I am not okay," they may search privately for language, understanding, or reassurance.
The Uplift Index studies this pre-disclosure layer of human behavior. It treats aggregated search behavior not as a diagnostic tool, but as a population-level signal that may help communities understand emerging needs earlier than conventional measurement systems allow.
Inquiry
Research Questions
Can aggregated digital behavior reveal emerging human needs before they become visible through traditional institutions?
Can search-derived signals complement surveys, public health data, economic indicators, and other traditional measures of population well-being?
What demographic, geographic, and temporal patterns emerge in search expressions related to human well-being, uncertainty, distress, and unmet need?
How might the shift from public search engines to private AI assistants affect society's ability to observe collective well-being and emerging social conditions?
Research Focus
AI, Observability, and Emerging Human Needs
Uplift explores how societies observe emerging human needs in an AI-mediated world. As more people turn to private AI systems rather than public search engines, the behavioral signals that have historically helped society understand itself may become less visible.
The project examines how collective needs become observable, how those signals evolve, and how societies can continue to understand emerging human needs as AI adoption grows.
Outputs
Current Research
Los Angeles Initial Observations
A preliminary regional analysis using anonymized, aggregated search-derived signals related to loneliness and emotional distress in Los Angeles County.
View observationsAI and Societal Measurement
An emerging research direction exploring how increasingly personalized AI systems may change the public behavioral signals societies use to understand themselves.
Read articleMethodology
Methodology and Ethical Commitments
The Uplift Index works only with aggregated, anonymized, population-level signals. It does not identify individuals, infer individual diagnoses, or attempt to intervene in private moments of distress.
The research is designed around several commitments:
- Population-level analysis only
- No personally identifiable information collected, stored, or analyzed
- No individual risk scoring
- No diagnostic claims
- Transparent methodology and limitations
- Responsible use of search-derived behavioral signals
Expanding the framework
Beyond Loneliness
The initial Uplift Index research focused on loneliness because it provided a clear and measurable starting point for investigating whether search behavior can reveal signals before formal disclosure. However, loneliness is only one example of a broader phenomenon.
The central question of the Uplift Index is whether large-scale digital behavior can help identify emerging societal needs before those needs become visible through traditional institutions, surveys, or public reporting systems.
Potential areas of future research include:
-
Financial Stress and Economic Anxiety
Early expressions of financial uncertainty, insecurity, and economic strain.
-
Social Connection and Community Health
Patterns related to belonging, isolation, relationships, and social trust.
-
Workforce and Career Uncertainty
Emerging concerns around employment, automation, retraining, and economic transition.
-
Public Health and Well-Being
Behavioral signals that may complement traditional health surveillance systems.
-
AI and Societal Measurement
How increasingly personalized AI systems may change the behavioral signals societies use to understand themselves.
The long-term objective is not to study any single issue, but to better understand how emerging human needs become visible through digital behavior and how those signals might responsibly complement traditional forms of societal measurement.
AI-mediated society
Why This Matters in an AI-Mediated World
Search engines have long functioned as an unintended mirror of collective human concern. People search for what they fear, need, cannot explain, or are not yet ready to say out loud. In aggregate, those searches can reveal patterns that surveys and institutions may miss.
As more people turn to conversational AI systems for guidance, emotional support, and private sense-making, some of these public behavioral signals may become less visible. Society may gain more personalized assistance while losing part of the shared mirror that helped researchers and institutions understand collective needs.
The Uplift Index is beginning to explore this transition: how population-level well-being can be measured responsibly in a world where more human expression is mediated by private, personalized AI systems.
About
About
Mark Hoashi is a technology founder and independent researcher with more than two decades of experience building digital platforms, consumer products, and data-driven systems. He holds an M.S. in Information Technology from Rochester Institute of Technology.
While completing his graduate studies, Mark developed Project 404 in collaboration with the National Center for Missing and Exploited Children, repurposing web infrastructure to surface information about missing children. His work has since spanned technology, consumer products, public policy, and large-scale digital systems.
The Uplift Index reflects a continuing interest in how meaningful signals emerge from complex systems and how those signals can be used to better understand human needs.