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The Representation of Jews, Judaism and Antisemitism in School Textbooks and Curricula in Europe Год публикации: 2025 Организация-автор: UNESCO Jewish communities have been integral to Europe’s social fabric for centuries, preserving rich religious and cultural traditions while facing recurring periods of exclusion, persecution, and resilience. School textbooks are important vantage points to understand how this history and heritage is represented, (de)constructed and embedded into a shared historical and cultural memory. They are also important practical tools used daily by students, teachers, and parents.This comprehensive research, carried out by UNESCO in collaboration with the Georg-Eckert-Institute and supported by funding from the European Commission, examines the ways in which Jewish culture, history, life, and anti-Jewish prejudice are represented in secondary school materials across eight European nations.The publication highlights opportunities within curricula to address Jewish experiences and antisemitism, reviews how these themes are incorporated into textbooks, and analyzes the narratives and portrayals that arise. The study also looks at the use of visual sources and assesses whether Jewish viewpoints and agency are sufficiently reflected. The findings highlight both recurring stereotypes and promising practices. By showcasing these contrasts, the study provides targeted recommendations to guide the creation of more inclusive educational materials. 
Challenging Systematic Prejudices: An Investigation into Bias Against Women and Girls in Large Language Models Год публикации: 2024 Автор: Daniel Van Niekerk | Maria Peréz Ortiz | John Shaw-Taylor | Davor Orlic | Ivana Drobnjak | Jackie Kay | Noah Siegel | Katherine Evans | Nyalleng Moorosi | Tina Eliassi-Rad | Leone Maria Tanczer | Wayne Holmes | Marc Peter Deisenroth | Isabel Straw | Maria Fasli | Rachel Adams | Nuria Oliver | Dunja Mladenić | Urvashi Aneja | Madeleine Janicky Организация-автор: UNESCO | International Research Centre on Artificial Intelligence (IRCAI) This study explores biases in three significant large language models (LLMs): OpenAI’s GPT-2 and ChatGPT, along with Meta’s Llama 2, highlighting their role in both advanced decision-making systems and as user-facing conversational agents. Across multiple studies, the brief reveals how biases emerge in the text generated by LLMs, through gendered word associations, positive or negative regard for gendered subjects, or diversity in text generated by gender and culture. The research uncovers persistent social biases within these state-of-the-art language models, despite ongoing efforts to mitigate such issues. The findings underscore the critical need for continuous research and policy intervention to address the biases that exacerbate as these technologies are integrated across diverse societal and cultural landscapes. The emphasis on GPT-2 and Llama 2 being open-source foundational models is particularly noteworthy, as their widespread adoption underlines the urgent need for scalable, objective methods to assess and correct biases, ensuring fairness in AI systems globally.