This is the introduction to a four-part series of posts detailing evidence of fraud in four academic papers co-authored by Harvard Business School Professor
Related work | Data Counterfactuals
Adjacent research areas and starting papers, curated in Semble and synced into the site.
Welcome to the Artificial Intelligence Incident Database
The starting point for information about the AI Incident Database
Deportation Data Project
The Deportation Data Project obtains, posts, and analyzes internal U.S. government immigration enforcement data via public records litigation.
Introducing the 2026 Cloudflare Threat Report
There has been a fundamental shift toward industrialized cyber threats, highlighted by a record 31.4 Tbps DDoS attack and sophisticated session token theft. Our new report examines how nation-states and criminal actors have moved beyond traditional exploits to "living off the XaaS" within legitimate enterprise logic.
Meta CIB / ATP Dashboard
A comprehensive intelligence platform analysing 214 coordinated inauthentic behaviour and adversarial threat operations identified by Meta across 70 countries, spanning April 2018 to Q4 2025.
AI Hallucination Cases Database – Damien Charlotin
Database tracking legal cases where generative AI produced hallucinated citations submitted in court filings.
Verification Handbook | DataJournalism.com
This book equips journalists with the knowledge to investigate on disinformation and media manipulation.
I think the data center backlash is mostly about data centers
It’s probably not your ideological thing, or mine. It’s about object-level claims about how data centers impact the communities where they’re built, many of which are wrong.
Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Detecting Non-Membership in LLM Training Data via Rank Correlations
As large language models (LLMs) are trained on increasingly vast and opaque text corpora, determining which data contributed to training has become essential for copyright enforcement, compliance auditing, and user trust. While prior work focuses on detecting whether a dataset was used in training (membership inference), the complementary problem—verifying that a dataset was not used- has received little attention. We address this gap by introducing PRISM, a test that detects dataset-level non-membership using only grey-box access to model logits. Our key insight is that two models that have not seen a dataset exhibit higher rank correlation in their normalized token log probabilities than when one model has been trained on that data. Using this observation, we construct a correlation-based test that detects non-membership. Empirically, PRISM reliably rules out membership in training data across all datasets tested while avoiding false positives, thus offering a framework for verifying that specific datasets were excluded from LLM training.
GRN · German Reproducibility Network
Working together for trustworthy and useful research
Explore Financial Disclosures From President Trump and 1,500 of His Appointees - ProPublica
Use this database to explore potential conflicts of interest for President Donald Trump and his team. The documents disclose positions officials have held outside government, their assets and their debts, among other things.
Semble — A social knowledge network for research
Follow your peers' research trails. Surface and discover new connections. Built on ATProto so you own your data.
ICE Detention Purchase Tracker
Data Studio turns your data into informative dashboards and reports that are easy to read, easy to share, and fully customizable.
Exposed Moltbook Database Let Anyone Take Control of Any AI Agent on the Site
'It exploded before anyone thought to check whether the database was properly secured.'
Data Centers - Database
Data Centers in Worldwide - List of Colocation and Cloud data facilities in Worldwide. Get Quotes and find Specs, Photos, Videos etc.
