Peloni: How might we find the means by which to defeat the imbalanced historic distortions waged against the Jewish people when imbalanced historic distortions are being pre-programmed into the modern public by the public source of intelligence which is used to fact check reality on a real-time basis? Similarly to having an international body in the UN acting as an unbiased arbiter in the international arena is undermined by its systemically antisemitic bias, having Jew Hatred hard wired into the basic understandings of Artificial Intelligence provides a new arena on which Israel, and Jews in general will have a new never ending battle against ignorance and disinformation which bakes our delegitimization, demonization and double-standards into the minds of the general public. As is true with the antisemitic United Nations, the bias programming in AI has the means to warp malicious predjudices against us on a host of venues even beyond the interplay of public opinion as explained below.
By: Fern Sidman | TJV | July 6, 2026
“Artificial Intelligence – Resembling Human Brain” by deepak pal, CC BY-SA 2.0
A groundbreaking new study has revealed a deeply troubling phenomenon at the intersection of technology and prejudice: artificial intelligence systems, widely regarded as impartial arbiters of information, may in fact be perpetuating centuries-old antisemitic tropes in subtle yet consequential ways. As reported and analyzed by The Times of Israel, researchers have found that large language models, including some of the most advanced and widely deployed systems in the world, appear to replicate long-standing cultural stereotypes about Jews, even when those identifiers are deliberately removed.
The study, published in the prestigious peer-reviewed journal American Psychologist, represents one of the most rigorous attempts to date to examine how bias manifests within artificial intelligence. Conducted by scholars from leading Israeli institutions, including Ben-Gurion University and Tel Aviv University, the research offers a sobering conclusion: “an ancient prejudice persists in modern technological systems through complex patterns of trait association and cultural coding.”
At the heart of the investigation lies a critical question that has increasingly occupied academics, policymakers, and technologists alike: as artificial intelligence becomes more deeply embedded in the fabric of modern life, to what extent does it inherit and reproduce the biases embedded in the vast bodies of human-generated data upon which it is trained?
Large language models, or LLMs, are the engines powering many of today’s most prominent artificial intelligence applications, including conversational systems such as ChatGPT. These models are trained on immense datasets comprising books, websites, academic literature, and other textual materials, enabling them to generate human-like responses with remarkable fluency and nuance. Yet, as The Times of Israel report noted, this very reliance on human-generated content renders them susceptible to absorbing and replicating the prejudices that have historically permeated such texts.
The researchers, led by Gal Gutman and Michael Gilead, sought to probe these latent biases through an innovative and carefully structured experimental design. Recognizing that modern AI systems are explicitly programmed to avoid producing overtly offensive or discriminatory language, the team devised a methodology to circumvent these safeguards and reveal underlying patterns.
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