This implementation follows the MakeAIVisible strategy: a privacy-first four-layer pipeline, six validated behavioral dimensions, and open collaboration through public GitHub challenges.
The data to understand AI's impact on teenagers already exists, but it remains inaccessible to families, educators, and independent researchers. Without transparent datasets and validated scoring, harmful influence patterns remain invisible.
Most evidence is trapped in closed platform logs. Public safety discussions rely on anecdotes rather than reproducible data. Teens and parents cannot independently evaluate long-term influence patterns because those patterns are not surfaced in an open, research-ready system.
Run a strict four-layer pipeline where PII is removed before storage, score every conversation across six behavioral dimensions, validate with human experts, and publish only anonymized aggregate outcomes with differential privacy.
The roadmap moves from foundational governance to anonymization, NLP scoring, dashboard publication, and academic validation. Each phase maps directly to open GitHub challenges.
Set up repositories, contribution templates, code of conduct, issue labels, and CI skeleton so community work can scale safely.
Ship the mobile-first submission portal and a production anonymization pipeline that strips PII before any persistence.
Deliver six-dimension scoring with a human-in-the-loop review queue using anonymized-only data access.
Release the AI Influence Map with differential privacy and anonymous token-based report delivery.
Finalize IRB-compatible consent workflows, methodology documentation, and ODbL dataset publishing pipelines.
Personally identifiable information is stripped before anything is stored. Raw logs do not survive anonymization. Controls are enforced in architecture, not only policy.
Mobile-friendly portal with no account and no identity collection. Accept imports from ChatGPT, Claude, Gemini, and Copilot exports.
Automated multilingual PII stripping on receipt with a 99.9%+ detection target; raw logs are destroyed immediately after anonymization.
Score each conversation 0-100 across six behavioral dimensions, then route anonymized records into human expert review.
Publish differential-privacy-protected aggregate insights on the AI Influence Map and deliver personal reports through anonymous tokens.
Each repository maps to a layer of the pipeline or supporting infrastructure. Pick one, claim an issue, and open a draft PR.
Collect — Submission Portal
Mobile-first, no-auth submission portal for ChatGPT, Claude, Gemini, and Copilot exports.
View on GitHub →Anonymize — PII Stripping Pipeline
Python service for multilingual PII detection and immediate raw-log destruction after anonymization.
View on GitHub →Analyze — Six-Dimension Scoring
NLP engine that scores conversations 0–100 across all six behavioral dimensions with expert validation support.
View on GitHub →Reveal — AI Influence Map
Public dashboard with differential-privacy-protected aggregates and cohort-level insights.
View on GitHub →Publish — ODbL Dataset Releases
Automated publishing pipeline for anonymized datasets to HuggingFace Datasets and DOI-linked archives.
View on GitHub →Govern — Community & Methodology
Contribution guidelines, governance model, methodology docs, and IRB-compatible consent frameworks.
View on GitHub →Every challenge is published as an issue with acceptance criteria and ownership labels. Comment to claim, open a draft PR early, and reference the issue in your PR.
Build multilingual PII detection and removal with strict false-negative controls before any data persistence.
Design correlation-resistant anonymous tokens with no identity lookup table and strong replay/abuse protections.
Score every conversation from 0-100 across six dimensions: Dependency/Reliance, Emotional Influence, Opinion Shaping, Epistemic Autonomy, Age-Appropriate Engagement, Transparency & Honesty Signaling.
Expand anonymization and scoring support for multilingual logs (ES, ZH, AR, and additional languages).
Build the AI Influence Map with privacy-preserving aggregates and clear cohort-level insights.
Create a human-in-the-loop review workflow where experts see only anonymized data and can calibrate scoring quality.
Improve no-auth submission flow with clearer export guidance and lower drop-off on mobile devices.
Deliver analysis reports to submitters without collecting identity and without correlation to raw submissions.
Design ethics and consent workflows suitable for minors while preserving anonymous data submission requirements.
Publish anonymized datasets under ODbL and mirror releases with DOI-linked publication flows.
Define contribution governance, issue ownership norms, and transparent decision-making for a distributed open project.
Establish validation protocols for scoring and publish defensible methodology so findings can be independently audited.
Follow a consistent open-source flow so work is reviewable, non-duplicative, and aligned with challenge milestones.
Pick one challenge issue and leave a comment before coding so duplicate effort is avoided.
Create your branch from your fork and keep each PR focused on one challenge or one acceptance criterion.
Open a draft PR early, link the issue in the description, and coordinate dependencies in GitHub Discussions.
Mark PR ready only after tests and checks pass, with clear notes on validation and edge cases.
Tag work with challenge labels (`challenge: pii`, `challenge: nlp`, `challenge: dashboard`, `challenge: privacy`, `challenge: research`, `challenge: community`).
Code, methods, and datasets should remain auditable and reusable under project licenses and governance rules.
Access tiers are enforced architecturally. Raw identifiers are never retained, while anonymized aggregates and methodology remain public and permanent.
Project managers only, and never stored long-term.
Public, permanent, and reusable by researchers and communities.
Pick a challenge, join Discussions, and help ship the next phase of the open pipeline.
No spam. No tracking pixels. No third-party analytics. We practice what we preach.