{"id":11303,"date":"2026-01-26T11:20:05","date_gmt":"2026-01-26T10:20:05","guid":{"rendered":"https:\/\/www.uni.lu\/c2dh-en\/?post_type=articles&#038;p=11303"},"modified":"2026-01-27T14:51:47","modified_gmt":"2026-01-27T13:51:47","slug":"how-ai-sees-the-past","status":"publish","type":"articles","link":"https:\/\/www.uni.lu\/c2dh-en\/articles\/how-ai-sees-the-past\/","title":{"rendered":"How AI sees the past: artificial intelligence and collective memory\u00a0"},"content":{"rendered":"\n<section class=\"wp-block-unilux-blocks-free-section section\"><div class=\"container xl:max-w-screen-xl\">\n<h3 class=\"has-text-align-left wp-block-unilux-blocks-heading\"        id=\"frederic-clavert-assistant-professor-in-contemporary-european-history-and-head-of-the-contemporary-european-history-research-group-at-the-c%c2%b2dh-is-using-ai-to-study-collective-memory\"\n    >\nFr\u00e9d\u00e9ric Clavert, Assistant Professor in Contemporary European History and Head of the Contemporary European History research group at the C\u00b2DH, is using AI to study collective memory.<\/h3>\n\n\n\n<p>Collective memory is a <strong>social phenomenon<\/strong>, shared through individuals and passed from one generation or group to the next. It both shapes a <strong>group\u2019s identity<\/strong> and <strong>influences the way in which that group understands or interprets the past.<\/strong><\/p>\n\n\n\n<p>Clavert is interested in this topic and how it intersects with AI, specifically chatbots. He is currently analysing <strong>prompts used to generate images via chatbots<\/strong>. \u201cIn a given prompt, there are <strong>references to the past<\/strong> that are linked to the user\u2019s specific views, and the result is generated by a system that was trained on datasets in which <strong>similar or different views of the past <\/strong>are embedded,\u201d Clavert explains. \u201cPlus there are layers of software that are added to the model to create the chatbot, where you can have <strong>filters <\/strong>that also influence the <strong>view of history embedded in the answer<\/strong>.\u201d<\/p>\n\n\n\n<section class=\"wp-block-unilux-blocks-quote-people\">\n    <div class=\"quote-people\" role=\"group\">\n        <figure class=\"quote-people__body\">\n            <blockquote class=\"quote-people__blockquote\">\n    <span class=\"quote-people__quote\">\u201f<\/span>\n    In a given prompt, there are references to the past that are linked to the user\u2019s specific views, and the result is generated by a system that was trained on datasets in which similar or different views of the past are embedded\u201d\n<\/blockquote><!-- People Item Automated: Quote People -->\n<div class=\"quote-people__visual\">\n    <figure class=\"wp-block-dev4-reusable-blocks-image object-fit--cover\">\n        <!-- Template Image Component: default -->\n<img decoding=\"async\" class=\"w-full\" width=\"\" height=\"\" rel=\"\" alt=\"Assist. Prof Fr\u00e9d\u00e9ric CLAVERT\" src=\"https:\/\/www.uni.lu\/en\/person-image\/NTAwMjkyNDBfX0Zyw6lkw6lyaWMgQ0xBVkVSVA==\" srcset=\"https:\/\/www.uni.lu\/en\/person-image\/NTAwMjkyNDBfX0Zyw6lkw6lyaWMgQ0xBVkVSVA==--thumbnail 150w,https:\/\/www.uni.lu\/en\/person-image\/NTAwMjkyNDBfX0Zyw6lkw6lyaWMgQ0xBVkVSVA==--medium 300w,https:\/\/www.uni.lu\/en\/person-image\/NTAwMjkyNDBfX0Zyw6lkw6lyaWMgQ0xBVkVSVA==--medium_large 768w,https:\/\/www.uni.lu\/en\/person-image\/NTAwMjkyNDBfX0Zyw6lkw6lyaWMgQ0xBVkVSVA==--large 1024w,https:\/\/www.uni.lu\/en\/person-image\/NTAwMjkyNDBfX0Zyw6lkw6lyaWMgQ0xBVkVSVA==--1536x1536 1536w,https:\/\/www.uni.lu\/en\/person-image\/NTAwMjkyNDBfX0Zyw6lkw6lyaWMgQ0xBVkVSVA==--2048x2048 2048w\" loading=\"lazy\" \/><!-- end Image Component -->\n    <\/figure>\n<\/div>\n<div class=\"quote-people__figcaption\">\n    <p class=\"quote-people__author\">Assist. Prof Fr\u00e9d\u00e9ric CLAVERT<\/p>\n    <p class=\"quote-people__position\">Assistant professor \/ Senior research scientist<\/p>\n    <div class=\"wp-block-unilux-blocks-simple-cta wp-block-unilux-blocks-people-item-automated\">\n    <a\n        href=\"https:\/\/www.uni.lu\/c2dh-en\/people\/frederic-clavert\/\"\n        title=\"Assist. Prof Fr\u00e9d\u00e9ric CLAVERT\"\n        class=\"link-text link-text--icon quote-people__link\"\n        target=\"\"\n    >\n        <span class=\"link-text__body\">\n            <span class=\"link-text__name\">View profile<\/span>\n        <\/span>\n        <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-right \"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-right\"><\/use><\/svg>    <\/a>\n<\/div>\n<\/div>\n<!-- end People Item Automated -->        <\/figure>\n    <\/div>\n<\/section>\n\n<div class=\"wp-block-unilux-blocks-spacer is-spacer-size-sm\"><\/div>\n\n\n<p>If users don\u2019t get the images they had hoped for, they can <strong>rephrase their prompts<\/strong>, a process Clavert calls \u201c<strong>negotiation with the bots<\/strong>\u201d. This process of negotiation, together with the <strong>confrontation between the views of the past that the user brings and the views of the past embedded within the AI platform<\/strong>, are particularly interesting for his work.<\/p>\n\n\n\n<h2 class=\"has-text-align-left wp-block-unilux-blocks-heading\"        id=\"popular-themes\"\n    >\nPopular themes<\/h2>\n\n\n\n<p>Depending on the <strong>AI source<\/strong>, obtaining data can be a challenge. Clavert\u2019s research has therefore focused on <strong>Stable Diffusion<\/strong>, an <strong>open-source text-to-image platform<\/strong> initially released in 2022. Given that Stable Diffusion is regularly used to train other systems, the platform provides open access to databases of user-generated prompts and their related images.<\/p>\n\n\n\n<p>For Clavert, this meant narrowing down a corpus of some <strong>10 million prompts and associated images<\/strong>. One challenge was to find a way of identifying all the prompts that reference the past, <strong>both implicitly and explicitly<\/strong>. Using a smaller sample of these prompts, Clavert then used the <strong>APIs of Claude AI and ChatGPT<\/strong> to further narrow down this subset, before performing <strong>quantitative and qualitative analysis with topic modelling<\/strong>.<\/p>\n\n\n\n<p>One striking example is a prompt describing <strong>European Union army tanks entering Budapest<\/strong>. Of course, the European Union doesn\u2019t have an army, so there\u2019s no reference to the actual past. But the prompt was written a few years ago, during a time of tough negotiations between Hungary and the European Commission when EU funding to Hungary had been blocked over the country\u2019s human rights violations. \u201cTheoretically, it\u2019s a prompt about the present, but if you put it into a search engine it generates images from the Hungarian Revolution of 1956 and the Soviet tanks sent to Budapest at that moment, so it\u2019s an implicit reference to the past,\u201d Clavert explains.<\/p>\n\n\n\n<p>Clavert has also focused on other prompts related to the EU to try to identify which <strong>concepts, people and moments<\/strong> from the past are associated with it. Interestingly, he has discovered that the EU tends to be associated with the Middle Ages \u2013 or at least a modern interpretation of the Middle Ages \u2013 and also empire and war. \u201cThere\u2019s always a part of the prompt that\u2019s more about the <strong>aesthetics of the image<\/strong> that the user is searching for,\u201d he adds. \u201cOne thing that is very popular is Soviet propaganda-style posters. Also since the data is from 2022 and 2023, the aggression against Ukraine features strongly.\u201d Other popular themes among the prompts he has reviewed are the World Wars, Napoleon Bonaparte and Arthurian legends.<\/p>\n\n\n\n<h2 class=\"has-text-align-left wp-block-unilux-blocks-heading\"        id=\"narratives-and-other-consideration\"\n    >\nNarratives and other consideration<\/h2>\n\n\n\n<p>Given that certain layers of the software are \u201c<strong>black boxes<\/strong>\u201d, it is not always clear why a chatbot provides a particular answer. In addition to filters, chatbots are also <strong>aligned with societal values<\/strong>. As Clavert explains, in <strong>authoritarian states<\/strong>, it can be easier to understand the filters on certain topics. On a <strong>Russian image generation platform<\/strong>, for instance, there is <strong>clear censorship on Ukraine<\/strong>. Meanwhile, the <strong>Chinese DeepSeek chatbot<\/strong> does not discuss certain political topics, such as <strong>Tiananmen Square<\/strong>.<\/p>\n\n\n\n<p>Clavert is aware that \u201c<strong>there could be a bias, depending on the corpus I\u2019m using<\/strong>,\u201d and that his corpus may be more European. Additionally, the corpus he is using is mainly in English. \u201cI\u2019m sure that if I used a corpus in Chinese, for instance, the themes would be quite different.\u201d<\/p>\n\n\n\n<p>If he had to provide a wish list for expanding his future research, Clavert would be interested in a <strong>partnership with one of the platforms<\/strong> and a <strong>bigger budget <\/strong>so he can research more prompts. He would also be keen to <strong>interview some users<\/strong> so he could glean more insights about their intentions when writing their prompts.<\/p>\n\n\n<section class=\"section section wp-block-unilux-blocks-quick-link-discover-section\">\n    <div class=\"container xl:max-w-screen-xl\">\n        \n<h2 class=\"has-text-align-left wp-block-unilux-blocks-heading\"        id=\"discover-more\"\n    >\nDiscover more<\/h2>\n\n<ul class=\"wp-block-unilux-blocks-quick-link-discover quick-link-list\">\n<li class=\"wp-block-unilux-blocks-quick-link-discover-item\">\n    <a\n                    href=\"https:\/\/www.uni.lu\/c2dh-en\/ai-at-c2dh\/\"\n                    class=\"quick-link\"\n            target=\"\"\n    >\n            <span class=\"quick-link__container\">\n                <span class=\"quick-link__text\">\n                    AI @ C\u00b2DH                <\/span>\n                <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--arrow-right \"><use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-right\"><\/use><\/svg>            <\/span>\n    <\/a>\n<\/li>\n<\/ul>\n    <\/div>\n<\/section><\/div><\/section>\n","protected":false},"excerpt":{"rendered":"<p>Assistant professor Fr\u00e9d\u00e9ric Clavert is using AI to study collective memory.<\/p>\n","protected":false},"author":57,"featured_media":11310,"template":"","format":"standard","meta":{"featured_image_focal_point":[],"show_featured_caption":true,"ulux_newsletter_groups":"","uluxPostTitle":"","uluxPrePostTitle":"","_trash_the_other_posts":false,"_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false},"articles-category":[472],"articles-topic":[405,400],"organisation":[],"authorship":[57],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.3 (Yoast SEO v22.3) - 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