Anglophone Narratives in Artificial Intelligence: Mapping Research on Cultural Bias and Ethical Concerns in Data-Driven Communication
Article Sidebar
Main Article Content
Abstract
This study presents a bibliometric analysis of English-language, Scopus-indexed scholarship on cultural bias and ethical concerns in artificial intelligence (AI)-driven communication, covering 1,919 documents published between 2015 and 2025. Using co-citation and co-word analyses conducted in VOSviewer, the study maps the intellectual structure and thematic development of this interdisciplinary field. The findings indicate exponential growth in scholarly output, particularly from 2023 onward, coinciding with the widespread adoption of generative AI tools and large language models. Co-citation analysis identified five thematic clusters: fairness toolkits and justice frameworks; algorithmic bias and word embeddings; explainable AI and algorithmic accountability; critical studies of race and inequality; and philosophical and filtering-based sources of bias. Co-word analysis reveals that while terms such as "artificial intelligence”, “algorithmic bias”, and “machine learning” dominate the literature, the cultural dimensions of bias are frequently addressed in isolation from technical and ethical frameworks, suggesting a fragmented research landscape in which technical optimisation tends to receive more attention than deeper socio-cultural analysis. This study is limited to English-language publications indexed in Scopus and does not include grey literature, non-English scholarship, or other databases; findings should therefore be interpreted as reflecting English-language, Scopus-indexed research rather than the full body of global scholarship on the topic. The study contributes a structured, data-driven map of a rapidly expanding field and identifies gaps that future interdisciplinary research may address, including the need for integrated frameworks that bridge technical, cultural, and ethical dimensions of AI in communication.
Article Details
References
AbuSahyon, A. S. A. E., Alzyoud, A., Alshorman, O., & Al-Absi, B. (2023). AI-driven technology and chatbots as tools for enhancing English language learning in the context of second language acquisition: A review study. International Journal of Membrane Science and Technology, 10(1), 1209-1223. https://doi.org/10.15379/ijmst.v10i1.2829
Adadi, A., & Berrada, M. (2018). Peeking inside the black box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052
Agu, E. E., Abhulimen, A. O., Obiki-Osafiele, A. N., Osundare, O. S., Adeniran, I. A., & Efunniyi, C. P. (2024). Discussing ethical considerations and solutions for ensuring fairness in AI-driven financial services. International Journal of Frontline Research in Multidisciplinary Studies, 3(2), 1–9. https://doi.org/10.56355/ijfrms.2024.3.2.0024
Akter, S., McCarthy, G., Sajib, S., Michael, K., Dwivedi, Y. K., D’Ambra, J., & Shen, K. N. (2021). Algorithmic bias in data-driven innovation in the age of AI. International Journal of Information Management, 60, Article 102387. https://doi.org/10.1016/j.ijinfomgt.2021.102387
Aninze, A. (2024). Artificial intelligence life cycle: The detection and mitigation of bias. International Conference on AI Research, 4(1), 40–49. https://doi.org/10.34190/icair.4.1.3131
Arjanto, P., Makulua, I. J., Sampe, P. D., Huliselan, N., & Ellis, R. (2025). Augmenting human connection: A systematic review of artificial intelligence in counseling practices, ethics, and cultural adaptation. Jurnal Bimbingan Dan Konseling Pandohop, 6(1), 1–15. https://doi.org/10.37304/pandohop.v6i1.20310
Awad, E., Dsouza, S., Kim, R., Schulz, J., Henrich, J., Shariff, A., Bonnefon, J.-F., & Rahwan, I. (2018). The Moral Machine experiment. Nature, 563(7729), 59–64. https://doi.org/10.1038/s41586-018-0637-6
Baeza-Yates, R. (2018). Bias on the web. Communications of the ACM, 61(6), 54–61. https://doi.org/10.1145/3209581
Bahroun, Z., Anane, C., Ahmed, V., & Zacca, A. (2023). Transforming education: A comprehensive review of generative artificial intelligence in educational settings through bibliometric and content analysis. Sustainability, 15(17), Article 12983. https://doi.org/10.3390/su151712983
Baird, A., & Schuller, B. (2020). Considerations for a more ethical approach to data in AI: On data representation and infrastructure. Frontiers in Big Data, 3, Article 25. https://doi.org/10.3389/fdata.2020.00025
Baldin, S. A. (2024). The impact of digital transformations in the economy and society on migration and public administration in the migration sphere. Ekonomika i Upravlenie: Problemy, Resheniya, 5/9(146), 6–13. https://doi.org/10.36871/ek.up.p.r.2024.05.09.001
Barnes, E., & Hutson, J. (2024). Navigating the ethical terrain of AI in higher education: Strategies for mitigating bias and promoting fairness. Forum for Education Studies, 2(2), Article 1229. https://doi.org/10.59400/fes.v2i2.1229
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
Bellamy, R. K. E., Dey, K., Hind, M., Hoffman, S. C., Houde, S., Kannan, K., Lohia, P., Martino, J., Mehta, S., Mojsilovic, A., Nagar, S., Ramamurthy, K. N., Richards, J., Saha, D., Sattigeri, P., Singh, M., Varshney, K. R., & Zhang, Y. (2019). AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias. IBM Journal of Research and Development, 63(4/5). https://doi.org/10.1147/JRD.2019.2942287
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623). Association for Computing Machinery. https://doi.org/10.1145/3442188.3445922
Benjamin, R. (2019). Race after technology: Abolitionist tools for the New Jim Code. Polity.
Bird, S., Dudík, M., Edgar, R., Horn, B., Lutz, R., Milan, V., Sameki, M., Wallach, H., & Walker, K. (2020). Fairlearn: A toolkit for assessing and improving fairness in AI (MSR-TR-2020-32). Microsoft Research.
Birhane, A. (2021). Algorithmic injustice: A relational ethics approach. Patterns, 2(2), Article 100205. https://doi.org/10.1016/j.patter.2021.100205
Bolukbasi, T., Chang, K.-W., Zou, J. Y., Saligrama, V., & Kalai, A. T. (2016). Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. Advances in Neural Information Processing Systems, 29, 4349–4357
Bozdag, E. (2013). Bias in algorithmic filtering and personalization. Ethics and Information Technology, 15(3), 209–227. https://doi.org/10.1007/s10676-013-9321-6
Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Proceedings of the Conference on Fairness, Accountability and Transparency (pp. 77–91). https://proceedings.mlr.press/v81/buolamwini18a.html
Bura, C., Kamatala, S., & Myakala, P. K. (2025). Ethical challenges in data science: Navigating the complex landscape of responsibility and fairness. International Journal of Current Science Research and Review, 8(3). https://doi.org/10.47191/ijcsrr/V8-i3-09
Burrell, J. (2016). How the machine “thinks”: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), Article 2053951715622512. https://doi.org/10.1177/2053951715622512
Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334), 183–186. https://doi.org/10.1126/science.aal4230
Chadha, K. S. (2024). Bias and fairness in artificial intelligence: Methods and mitigation strategies. International Journal for Research Publication and Seminar, 15(3), 36–49. https://doi.org/10.36676/jrps.v15.i3.1425
Chinta, S. V., Wang, Z., Palikhe, A., Zhang, X., Kashif, A., Smith, M. A., Liu, J., & Zhang, W. (2024). AI-driven healthcare: A review on ensuring fairness and mitigating bias (Version 2). arXiv. https://doi.org/10.48550/ARXIV.2407.19655
Chouldechova, A., & Roth, A. (2018). The frontiers of fairness in machine learning (Version 1). arXiv. https://doi.org/10.48550/ARXIV.1810.08810
Curto, G., Jojoa Acosta, M. F., Comim, F., & Garcia-Zapirain, B. (2024). Are AI systems biased against the poor? A machine learning analysis using Word2Vec and GloVe embeddings. AI & Society, 39(2), 617–632. https://doi.org/10.1007/s00146-022-01494-z
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012). Fairness through awareness. In Proceedings of the 3rd Innovations in Theoretical Computer Science Conference (pp. 214–226). Association for Computing Machinery. https://doi.org/10.1145/2090236.2090255
Ejjami, R. (2024). The adaptive personalization theory of learning: Revolutionizing education with AI. Journal of Next-Generation Research 5.0. https://doi.org/10.70792/jngr5.0.v1i1.8
Ezeaka, N. B., & Umennebuaku, V. A. (2024). Development communication in the artificial intelligence (AI) era: Navigating cultural complexity and technological advancements. African Journal of Culture, History, Religion and Traditions, 7(3), 98–107. https://doi.org/10.52589/AJCHRT-HQCS2XJ7
Grinceviciene, V., Bareviciute, J., Asakaviciute, V., & Targamadze, V. (2019). Equal opportunities and dignity as values in the perspective of I. Kant’s deontological ethics: The case of inclusive education. Filosofija, Sociologija, 30(1), 80–88. https://doi.org/10.6001/fil-soc.v30i1.3919
Gurkan, N., & Suchow, J. W. (2024). Exploring public opinion on responsible AI through the lens of cultural consensus theory. arXiv. https://doi.org/10.48550/ARXIV.2402.00029
Hoffmann, A. L., Roberts, S. T., Wolf, C. T., & Wood, S. (2018). Beyond fairness, accountability, and transparency in the ethics of algorithms: Contributions and perspectives from LIS. Proceedings of the Association for Information Science and Technology, 55(1), 694–696. https://doi.org/10.1002/pra2.2018.14505501084
Huriye, A. Z. (2023). The ethics of artificial intelligence: Examining the ethical considerations surrounding the development and use of AI. American Journal of Technology, 2(1), 37–45. https://doi.org/10.58425/ajt.v2i1.142
Ibrahim, Z., Mohamed, J., Kassim, N., & Bahrudin, I. A. (2023). The assistive technology for teaching and learning of social skills for autism spectrum disorder children: Multimedia interactive social skills module application. European Journal of Educational Research, 12(3), 1465–1477. https://doi.org/10.12973/eu-jer.12.3.1465
Inuwa-Dutse, I. (2023). FATE in AI: Towards algorithmic inclusivity and accessibility. In Equity and Access in Algorithms, Mechanisms, and Optimization (pp. 1–14). Association for Computing Machinery. https://doi.org/10.1145/3617694.3623233
Konidena, B. K., Malaiyappan, J. N. A., & Tadimarri, A. (2024). Ethical considerations in the development and deployment of AI systems. European Journal of Technology, 8(2), 41–53. https://doi.org/10.47672/ejt.1890
Mohanty, S. (2025). Fine-grained bias detection in LLM: Enhancing detection mechanisms for nuanced biases (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2503.06054
Molla, M. A. M., & Ahsan, M. M. (2025). Artificial intelligence and journalism: A systematic bibliometric and thematic analysis of global research (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2507.10891
Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M.-E., Ruggieri, S., Turini, F., Papadopoulos, S., Krasanakis, E., Kompatsiaris, I., Kinder-Kurlanda, K., Wagner, C., Karimi, F., Fernandez, M., Alani, H., Berendt, B., Kruegel, T., Heinze, C., … Staab, S. (2020). Bias in data-driven AI systems: An introductory survey (arXiv:2001.09762). arXiv. https://doi.org/10.48550/arXiv.2001.09762
Osasona, F., Amoo, O. O., Atadoga, A., Abrahams, T. O., Farayola, O. A., & Ayinla, B. S. (2024). Reviewing the ethical implications of AI in decision-making processes. International Journal of Management & Entrepreneurship Research, 6(2), 322–335. https://doi.org/10.51594/ijmer.v6i2.773
Pasipamire, N., & Muroyiwa, A. (2024). Navigating algorithm bias in AI: Ensuring fairness and trust in Africa. Frontiers in Research Metrics and Analytics, 9, Article 1486600. https://doi.org/10.3389/frma.2024.1486600
Patel, D. B. (2021). Ethical AI: Addressing bias and fairness in machine learning models for decision-making. Journal of Computer Science and Technology Studies, 3(1), 13–17. https://doi.org/10.32996/jcsts.2021.3.1.3
Peng, Z., Fu, R. Z., Chen, H. P., Takahashi, K., Tanioka, Y., & Roy, D. (2024). AI applications in emotion recognition: A bibliometric analysis. SHS Web of Conferences, 194, Article 03005. https://doi.org/10.1051/shsconf/202419403005
Polo, E. P., & Ailodion, D. O. (2025). Tackling racial bias in AI systems: Applying the bioethical principle of justice and insights from Joy Buolamwini’s “Coded Bias” and the “Algorithmic Justice League.” Bangladesh Journal of Bioethics, 16(1), 8–14. https://doi.org/10.62865/bjbio.v16i1.129
Ramamoorthy, L. (2025). Evaluating generative AI: Challenges, methods, and future directions. International Journal for Multidisciplinary Research, 7(1), Article 37182. https://doi.org/10.36948/ijfmr.2025.v07i01.37182
Ratna, J., Kalnawat, A., Pawar, A. M., Jadhav, V. D., Srilatha, P., & Khetani, V. (2024). Transparency in algorithmic decision-making: Interpretable models for ethical accountability. E3S Web of Conferences, 491, Article 02041. https://doi.org/10.1051/e3sconf/202449102041
Robledo-Giraldo, S., Figueroa-Camargo, J. G., Zuluaga-Rojas, M. V., Vélez-Escobar, S. B., & Duque-Hurtado, P. L. (2023). Mapping, evolution, and application trends in co-citation analysis: A scientometric approach. Revista de Investigación, Desarrollo e Innovación, 13(1), 201–214. https://doi.org/10.19053/20278306.v13.n1.2023.16070
Saiz-Alvarez, J. (2024). Innovation management: A bibliometric analysis of 50 years of research using VOSviewer and Scopus. World, 5(4), Article 46. https://doi.org/10.3390/world5040046
Samuel-Okon, A. D. (2024). Smart media or biased media: The impacts and challenges of AI and big data on the media industry. Asian Journal of Research in Computer Science, 17(7), 128–144. https://doi.org/10.9734/ajrcos/2024/v17i7484
Shahzad, K., Khan, S., Iqbal, A., & Javeed, A. (2024). Identifying university librarians’ readiness to adopt artificial intelligence (AI) for innovative learning experiences and smart library services: An empirical investigation. Global Knowledge, Memory and Communication. https://doi.org/10.1108/gkmc-12-2023-0496
Shuford, J. (2024). Exploring ethical dimensions in AI: Navigating bias and fairness in the field. Journal of Artificial Intelligence General Science, 3(1), 103–124. https://doi.org/10.60087/jaigs.vol03.issue01.p124
Shukla, S. (2024). Principles governing ethical development and deployment of AI. International Journal of Engineering, Business and Management, 8(2), 26–46. https://doi.org/10.22161/ijebm.8.2.5
Singh, C., Dash, M. K., Sahu, R., & Kumar, A. (2024). Artificial intelligence in customer retention: A bibliometric analysis and future research framework. Kybernetes, 53(11), 4863–4888. https://doi.org/10.1108/K-02-2023-0245
Tao, H., Wan, Y., Huang, L., & Zhao, Y. (2025). Applications and challenges of artificial intelligence in international trade and logistics: Current status, future developments, and policy recommendations. International Journal of Computer Science and Information Technology, 5(1), 35–47. https://doi.org/10.62051/ijcsit.v5n1.04
Thakur, N., & Sharma, A. (2024). Ethical considerations in AI-driven financial decision making. Journal of Management & Public Policy, 15(3), 41–57. https://doi.org/10.47914/jmpp.2024.v15i3.003
Venkatasubbu, S., & Krishnamoorthy, G. (2022). Ethical considerations in AI addressing bias and fairness in machine learning models. Journal of Knowledge Learning and Science Technology, 1(1), 130–138. https://doi.org/10.60087/jklst.vol1.n1.p138
Vindigni, G. (2025). Gender bias and cultural misrepresentation in AI: A critical inquiry into cross-cultural communication and algorithmic design. European Journal of Applied Science, Engineering and Technology, 3(3), 51–72. https://doi.org/10.59324/ejaset.2025.3(3).06
Wakunuma, K., & Eke, D. (2024). Africa, ChatGPT, and generative AI systems: Ethical benefits, concerns, and the need for governance. Philosophies, 9(3), Article 80. https://doi.org/10.3390/philosophies9030080
Weiner, E. B., Dankwa-Mullan, I., Nelson, W. A., & Hassanpour, S. (2025). Ethical challenges and evolving strategies in the integration of artificial intelligence into clinical practice. PLOS Digital Health, 4(4), Article e0000810. https://doi.org/10.1371/journal.pdig.0000810
Zhang, C. (2024). AI in education: Opportunities, challenges, and pathways for equitable learning. Journal of Education, Humanities and Social Sciences, 45, 723–728. https://doi.org/10.54097/kfgp6j07