{"id":267,"date":"2025-11-27T20:17:49","date_gmt":"2025-11-28T01:17:49","guid":{"rendered":"https:\/\/pressbooks.bccampus.ca\/ccdw\/?post_type=chapter&p=267"},"modified":"2025-11-29T18:29:34","modified_gmt":"2025-11-29T23:29:34","slug":"familiar-themes","status":"publish","type":"chapter","link":"https:\/\/pressbooks.bccampus.ca\/ccdw\/chapter\/familiar-themes\/","title":{"raw":"Familiar themes and trends in the Fourth Age","rendered":"Familiar themes and trends in the Fourth Age"},"content":{"raw":"Data compiled using the Dimensions<\/em> analytics platform reveals the explosive growth in number of publications located using the search terms \u2018generative artificial intelligence\u2019 AND \u2018cultural diversity\u2019 since 2020, in the extensive database of academic literature to which it has access (Figure 3). Meanwhile, a guided survey of recent literature relevant to AI or generative AI and cultural difference or cultural diversity using SciSpace<\/em> shows jumps in related topic trends in academic work beginning in 2023 (Figure 4) (note that both data samples must be interpreted as indicative, and not exhaustive).\r\n\r\n[caption id=\"attachment_107\" align=\"aligncenter\" width=\"550\"]\"\" Figure 3. Count of academic publications referring to \u201c\u2018generative artificial intelligence\u2019 AND \u2018cultural diversity\u2019\u201d 2020-2025<\/strong>[\/caption]\r\n\r\n[caption id=\"attachment_110\" align=\"aligncenter\" width=\"550\"]\"\" Figure 4. Topic trends in academic literature relevant to AI or generative AI AND cultural difference or cultural diversity<\/strong>[\/caption]\r\n\r\nPerhaps unsurprisingly, a high-level overview of very recent publications (many still only available as preprints) reveals themes familiar to us from earlier decades of internet research when previous technological developments also brought about tumultuous change. Recent \u2018alignment studies\u2019, for example, have reported on misalignment of generative AI with national or cultural values<\/strong> (interestingly, Globig et al. (2024) report in their preprint that citizens in Western countries declare greater skepticism and perceive greater misalignment). LLMs are reported to\u00a0 exhibit systematic value orientations<\/strong> based on the dominant cultural signals used in their training (they are not \u2018neutral\u2019) (Agarwal et al., 2024; Fenech-Borg et al., 2025). Concern is expressed about the moral and cultural homogenizing force<\/strong> of generative AI tools, and their failure to reflect culturally <\/strong>diverse values<\/strong> (Kharchenko et al., 2024; Meijer et al., 2024). With relation to output, text\u2011to\u2011image and multimodal models are reported to miss cultural expectations<\/strong> frequently (Johnson et al., 2022; Nayak et al., 2025). Researchers observe stereotyping, exoticism, and erasure of nuance in LLM output<\/strong> (Nayak et al., 2025) and AI suggestions are found to steer writers toward Western styles (Liu, 2025). Recent work does also report some success with early mitigation efforts<\/strong>, for example, fine\u2011tuning with culturally relevant corpora, language\u2011specific tuning, iterative prompting, and culture\u2011aware pipelines to improve cultural alignment in targetted settings. Broad coverage remains challenging, however<\/strong>. As the literature of the Fourth Age matures, it will be interesting to discover whether these patterns hold.\r\n\r\n ","rendered":"

Data compiled using the Dimensions<\/em> analytics platform reveals the explosive growth in number of publications located using the search terms \u2018generative artificial intelligence\u2019 AND \u2018cultural diversity\u2019 since 2020, in the extensive database of academic literature to which it has access (Figure 3). Meanwhile, a guided survey of recent literature relevant to AI or generative AI and cultural difference or cultural diversity using SciSpace<\/em> shows jumps in related topic trends in academic work beginning in 2023 (Figure 4) (note that both data samples must be interpreted as indicative, and not exhaustive).<\/p>\n

\"\"
Figure 3. Count of academic publications referring to \u201c\u2018generative artificial intelligence\u2019 AND \u2018cultural diversity\u2019\u201d 2020-2025<\/strong><\/figcaption><\/figure>\n
\"\"
Figure 4. Topic trends in academic literature relevant to AI or generative AI AND cultural difference or cultural diversity<\/strong><\/figcaption><\/figure>\n

Perhaps unsurprisingly, a high-level overview of very recent publications (many still only available as preprints) reveals themes familiar to us from earlier decades of internet research when previous technological developments also brought about tumultuous change. Recent \u2018alignment studies\u2019, for example, have reported on misalignment of generative AI with national or cultural values<\/strong> (interestingly, Globig et al. (2024) report in their preprint that citizens in Western countries declare greater skepticism and perceive greater misalignment). LLMs are reported to\u00a0 exhibit systematic value orientations<\/strong> based on the dominant cultural signals used in their training (they are not \u2018neutral\u2019) (Agarwal et al., 2024; Fenech-Borg et al., 2025). Concern is expressed about the moral and cultural homogenizing force<\/strong> of generative AI tools, and their failure to reflect culturally <\/strong>diverse values<\/strong> (Kharchenko et al., 2024; Meijer et al., 2024). With relation to output, text\u2011to\u2011image and multimodal models are reported to miss cultural expectations<\/strong> frequently (Johnson et al., 2022; Nayak et al., 2025). Researchers observe stereotyping, exoticism, and erasure of nuance in LLM output<\/strong> (Nayak et al., 2025) and AI suggestions are found to steer writers toward Western styles (Liu, 2025). Recent work does also report some success with early mitigation efforts<\/strong>, for example, fine\u2011tuning with culturally relevant corpora, language\u2011specific tuning, iterative prompting, and culture\u2011aware pipelines to improve cultural alignment in targetted settings. Broad coverage remains challenging, however<\/strong>. As the literature of the Fourth Age matures, it will be interesting to discover whether these patterns hold.<\/p>\n

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