{"id":38940,"date":"2025-10-24T16:53:34","date_gmt":"2025-10-24T11:23:34","guid":{"rendered":"https:\/\/www.editage.com\/insights\/?p=38940"},"modified":"2025-12-18T11:13:45","modified_gmt":"2025-12-18T05:43:45","slug":"did-ai-create-new-knowledge-silos-voices-from-around-the-world","status":"publish","type":"post","link":"https:\/\/www.editage.com\/insights\/did-ai-create-new-knowledge-silos-voices-from-around-the-world","title":{"rendered":"Did AI Create New Knowledge Silos? Voices from Around the World"},"content":{"rendered":"

As the world marks Open Access Week<\/strong><\/a>, the research community once again turns its attention to the free and equitable exchange of knowledge. This year, the conversation extends beyond journals and paywalls to a newer frontier:\u00a0 artificial intelligence (AI)<\/strong>. Once hailed as the ultimate democratizer of information, AI now shapes how we discover, interpret, and share research. But in doing so, it also raises a pressing question: has AI created new knowledge silos, even as we strive for openness?<\/em><\/p>\n

Artificial intelligence was supposed to be the great equalizer\u2014an invisible force that made knowledge truly borderless! Yet as AI models become increasingly complex, a new question is emerging: has AI actually created new knowledge silos?<\/p>\n

Across disciplines and geographies, thought-leaders are reflecting on how AI reshapes the global information ecosystem. Their perspectives reveal a paradox\u2014AI connects us, but it can also quietly divide. We looked around and have curated the voices of some thought-leaders from around the world on this subject.<\/p>\n

    \n
  1. The Algorithmic Gatekeepers<\/strong><\/li>\n<\/ol>\n

    AI ethicist Timnit Gebru has long argued that large language models are not neutral tools. They mirror the inequalities of the data they are built on. When models are trained mostly on English-language and Western datasets, entire bodies of knowledge from other parts of the world remain invisible. (Harvard Business School<\/a>)
    \nGebru\u2019s advocacy for transparency and community-led AI development reminds us that democratizing AI isn\u2019t about open APIs alone\u2014it\u2019s about who gets represented in the data to begin with.<\/p>\n

      \n
    1. The Echo Chamber Effect<\/strong><\/li>\n<\/ol>\n

      Social scientist Sinan Aral highlights how algorithmic and personalized content flows narrow our information diets. His concept of the \u201chype machine loop\u201d explains how social-media algorithms tend to show us more of what we already like, reducing exposure to diverse viewpoints. (CHM<\/a>)
      \nWhile AI helps us find content that feels relevant, it also narrows what we see. The same personalization that makes a feed feel smart can quietly silo us from global perspectives\u2014even in academic and research spaces.<\/p>\n

        \n
      1. The Great Data Divide<\/strong><\/li>\n<\/ol>\n

        Fei-Fei Li, co-director of Stanford\u2019s Human-Centered AI Institute, warns of a widening \u201cdata divide.\u201d Only a handful of corporations and elite institutions have the resources to collect, label, and train data at scale. That means innovation\u2014once the hallmark of open collaboration\u2014now depends on who controls the pipelines of information. (McKinsey & Company<\/a>)
        \nLi\u2019s vision of human-centered AI stresses inclusivity and shared governance to prevent knowledge monopolies.<\/p>\n

          \n
        1. Open Science at Risk<\/strong><\/li>\n<\/ol>\n

          Open-access champion Johan Rooryck, Executive Director of cOAlition S, believes AI could undermine open science if it locks insights behind proprietary systems. If AI-generated insights cannot be audited or cited, we risk losing transparency\u2014one of the foundations of scientific knowledge sharing.<\/p>\n

            \n
          1. Missing Voices<\/strong><\/li>\n<\/ol>\n

            Cognitive scientist Abeba Birhane describes this as \u201cepistemic exclusion\u201d\u2014a phenomenon where local knowledge from the Global South rarely enters AI datasets or benchmarks. (WIRED<\/a>)
            \nWhen these perspectives are missing, entire communities remain invisible in the world that AI depicts.<\/p>\n

              \n
            1. Building Bridges in Asia<\/strong><\/li>\n<\/ol>\n

              Across Asia, a quiet counter-current is forming. In Japan and Korea, language-specific AI models (such as rinna<\/em><\/a> and HyperCLOVA<\/em><\/a>) are helping researchers work in their native languages while still connecting to global networks. These efforts aim to democratize AI knowledge for non-English-speaking researchers and break the linguistic silos that global AI has often reinforced.<\/p>\n

                \n
              1. Editorial AI as Equalizer<\/strong><\/li>\n<\/ol>\n

                According to Shilpi Mehra at Paperpal<\/a>, \u201cAI can either deepen divides or close them\u2014depending on how we use it.\u201d By supporting multilingual research writing and improving accessibility, editorial AI tools<\/a> can help level the playing field for researchers who lack institutional support or English fluency. Rather than replacing editors, such tools amplify accessibility and inclusion.<\/p>\n

                The Way Forward<\/strong><\/p>\n

                AI didn\u2019t set out to build new silos. But by concentrating data, code and computation in limited hands, it risks doing just that. Breaking these barriers will require collaboration between technologists, publishers and research communities to ensure transparency, diversity and openness. The future of knowledge shouldn\u2019t depend on who owns the algorithms\u2014but on who contributes to them.<\/p>\n","protected":false},"excerpt":{"rendered":"

                As the world marks Open Access Week, the research community once again turns its attention to the free and equitable exchange of knowledge. This year, the conversation extends beyond journals and paywalls to a newer frontier:\u00a0 artificial intelligence (AI). Once hailed as the ultimate democratizer of information, AI now shapes how we discover, interpret, and […]<\/p>\n","protected":false},"author":2830,"featured_media":38942,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[2430,2392],"tags":[2923,2743,448,1987],"new_categories":[],"new_tags":[5763,5931],"series":[],"class_list":["post-38940","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-open-access-subscription-models","category-research-data-management","tag-ai","tag-ai-for-researchers","tag-international-open-access-week","tag-knowledge-data-sharing","new_tags-ai-for-researchers","new_tags-open-access"],"acf":[],"yoast_head":"\nDid AI Create New Knowledge Silos? Voices from Around the World | Editage Insights<\/title>\n<meta name=\"description\" content=\"AI was meant to democratize information. 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