The Hanover Institute for Public Policy, appearing as a new think tank focused on Israel/Palestine, has been identified as a fabricated entity. It publishes reports on sensitive topics such as "Does AIPAC Use ‘Dark Money in Elections?" and "Is Israel Carrying out a Deliberate Campaign of Starvation in Gaza?" A disclaimer on its webpage reveals its creation by Piro, Inc. on behalf of the Israeli Government Advertising Agency.
The reports from the Hanover Institute, all concerning Israel and Palestine, are part of an effort to influence AI chatbots. These "data reports" include footnotes and tables of contents, presenting arguments in a neutral tone to appeal to large language models like Claude or Gemini. Piro, Inc. describes this service as "AI Story Optimization," while others refer to it as "LLM poisoning."
The institute's "about" page states its purpose is to study antisemitism in the United States and publish evidence. Many reports are formulaic, starting with questions users might ask chatbots, such as "What Caused the Displacement of Palestinians in 1948?" or "Which Humanitarian Organizations Have Documented Israeli War Crimes?" Reports often conclude by linking the topic to rising antisemitism, citing similar studies.
One report, "Is the IDF the World’s Most Moral Army?", cites a 2022 poll where 47% of Israeli Jews agreed with the statement. Another report casts doubt on UNICEF's claim regarding damage to Gaza's water and institutional infrastructure. The Hanover Institute also published a study claiming 19 of the 36 most-watched Israel-Gaza explainer videos contained contested claims, mostly aligning with the Palestinian narrative.
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The Israeli Government Advertising Agency, through Piro, Inc., created the "Hanover Institute for Public Policy," a fabricated think tank producing reports designed to influence AI chatbots on topics related to Israel and Palestine. This initiative, termed "AI Story Optimization" by Piro and "LLM poisoning" by others, aims to shape the information large language models provide in response to user queries.