This report details the real human impact of some of the sites Cloudflare sustains and evaluates the systemic policy and enforcement gaps that allow them to remain operational.
6 Results
ADL study finds popular open-source LLMs can easily be manipulated by malicious actors to produce antisemitic, extremist, and dangerous content amid weak safety guardrails.
Artificial intelligence is rapidly reshaping how people consume and trust information—including what books they read. Amazon, the world’s largest bookseller, uses Large Language Models (LLMs) to generate short, snappy summaries of customer reviews. While this may be useful when applied to bedsheets or kitchen appliances, applying AI to book reviews—without human oversight—is proving to be deeply problematic. We found that AI-generated reviews are promoting books that…
Co-produced with Builders For Tomorrow Executive Summary ADL has identified extensive issues with antisemitic and anti-Israel bias on Wikipedia in multiple languages. These issues include 1) a coordinated campaign to manipulate Wikipedia content related to Israel, the Israeli-Palestinian conflict, and similar issues, in which a group of editors systematically evade Wikipedia’s rules to shift balanced narratives toward skewed ones, spotlighting criticism of Israel and downplaying…
Executive Summary Platforms are still failing to take action on antisemitic hate reported through regular channels available to users. Researchers at the ADL Center for Technology and Society (CTS) tested how well five major platforms enforced their policies against hateful content in two areas: antisemitic conspiracy theories and the term “Zionist” used as a slur. Most platforms only took action when ADL escalated the reports through direct channels, and even…
The first analysis of hate and harassment on Minecraft server data.