About Digital Harms Tracker
What is the Digital Harms Tracker?
The Digital Harms Tracker is a comprehensive database documenting verified cases where individuals have been harmed by digital platforms, AI systems, and cryptocurrency. We track incidents ranging from algorithm-driven radicalization and deepfake fraud to dangerous viral challenges and gig economy worker exploitation.
Our mission is to provide researchers, journalists, policymakers, and the public with structured, verifiable data about the real-world harms caused by technology platforms and systems. By making this information accessible and searchable, we aim to support evidence-based policy discussions and accountability.
This project is not advocacy — it is evidence collection. We do not take positions on policy solutions, but rather document what has happened, where, and to whom, based on credible reporting and official records.
Methodology
Data Collection: Our data collection methodology has evolved to combine historical research with real-time monitoring:
- Pre-2026 Historical Data: Incidents occurring before January 1, 2026 were identified and catalogued through a combination of AI-powered research agents and human coding with oversight. This systematic review of historical sources ensures comprehensive coverage of documented digital harms.
- 2026 Forward - Live Monitoring: Starting January 1, 2026, incidents are collected automatically every hour from global RSS feeds covering major news outlets, technology publications, and investigative journalism sources. This real-time monitoring ensures rapid identification of emerging incidents.
Data Sources: We monitor major news outlets, investigative journalism, academic research, government reports, court filings, and official incident disclosures. Every incident in our database is linked to at least one credible primary or secondary source.
Verification: We only include incidents that have been reported by credible sources with specific details about what happened, when, and to whom. We do not include rumors, unverified claims, or incidents lacking sufficient documentation. All AI-identified incidents undergo human review before publication.
Categorization: Incidents are tagged by domain (Social Media, AI Systems, Cryptocurrency, Gaming, Gig Economy), platform, harm type, severity, and other metadata to enable systematic analysis and pattern identification. This structured approach enables researchers to identify trends and patterns across time, platforms, and harm types.
Updates: The database is updated continuously as new incidents are reported. We also update existing entries when new information becomes available, such as legal outcomes or platform responses.
How We Use AI
Our system uses AI to process large volumes of news articles efficiently while maintaining high accuracy through a multi-stage pipeline with human oversight:
Stage 1: Content Collection
Every hour, our system automatically monitors global RSS feeds from major news outlets and technology publications. New articles are collected, deduplicated, and queued for analysis.
Stage 2: Relevance Classification
AI models (Google Gemini) read each article and classify its relevance to digital harms. The AI determines whether the article documents a specific incident where people were harmed by a digital platform, as opposed to general tech news or policy discussions.
Stage 3: Entity Extraction
For relevant articles, AI performs Named Entity Recognition (NER) to extract structured data: platforms involved, companies responsible, harm types, victim details, dates, locations, and financial losses. It also generates a factual summary of the incident.
Stage 4: Human Review
All AI-classified incidents are reviewed by human analysts before being marked as "analyzed" and published to the public database. This ensures accuracy and catches edge cases that AI might misclassify.
Why this approach? Manual review of every news article about technology is impractical at scale. AI enables us to process hundreds of articles daily while human oversight ensures quality. This hybrid approach combines the efficiency of automation with the judgment and verification that only humans can provide.
Who Maintains This?
The Digital Harms Tracker is maintained by a team of researchers and technologists committed to transparency and public accountability in the technology sector. Our work is independent and not funded by any platform, company, or advocacy organization.
We believe that systematic documentation of digital harms is essential for informed public discourse and evidence-based policymaking. By making this data freely accessible, we hope to support researchers, journalists, and policymakers working to understand and address these issues.
Data Sources
Our data comes from:
- Major news organizations and investigative journalism outlets
- Peer-reviewed academic research and research institution reports
- Government agencies and regulatory bodies
- Court filings and legal documents
- Official incident reports and transparency disclosures from platforms
- Non-profit research organizations and child safety groups
Every incident entry includes a citation to the original source, allowing users to verify the information and access additional context.
Contact
We welcome corrections, additional verified incidents, and feedback on our methodology. If you are a researcher, journalist, or policymaker who would like to use our data or collaborate, please reach out.
Note: This is a placeholder email for the static demo. In production, this would link to a real contact form or email address.