A Sociological Eye on AI. Key Concepts, Analytical Dimensions, and Methodological Tensions

Marco Solaroli; Emma Garzonio

doi:10.60923/issn.1971-8853/23907

Authors

Marco Solaroli Department of the Arts, University of Bologna https://orcid.org/0000-0001-5433-1540
Marco Solaroli is an Associate Professor of Sociology of Culture and Communication in the Department of the Arts at the University of Bologna. He studies cultural production, innovation, valuation, and consecration in relation to the digital transformation of media and culture. His current research projects deal with news photography and global crises; social media images and political power; generative AI and visual memory.
Emma Garzonio Department of Philosophy, Communication and Performing Arts, Roma Tre University https://orcid.org/0000-0001-7047-9289
Emma Garzonio is a Research fellow in the Department of Philosophy, Communication and Performing Arts at Roma Tre University (Italy). Her research interests focus on the sociology of media, political communication, AI, and digital visual culture.

DOI:

https://doi.org/10.60923/issn.1971-8853/23907

Keywords:

Artificial Intelligence (AI), Artificial agency, Artificial communication, Artificial memory, Generative Artificial Intelligence (GenAI), Sociology of AI

Abstract

Between 2024 and 2026, a variety of position papers and special issues in the field of social sciences have started addressing the social, epistemological, and cultural impact of generative AI. Such emerging literature looks vast, heterogeneous, fragmented, and expectably still at a pre-paradigmatic stage. Without the ambition of addressing such a fast-growing body of work in a fully systematic way, this paper reviews a significant variety of recent publications to identify major trends and some common ground aimed at bridge-building among different intra-disciplinary research strands, while remaining open to inter-disciplinary dialogue. It proposes a framework based on a few key concepts (AI as agency, communication, and memory), analytical dimensions (AI as object, practice, and method), and methodological tensions (macro-micro, quanti-quali, distant-close). In doing so, it contributes to the ongoing construction of a shared toolkit to investigate how various social and cultural domains have been affected by the recent development and increasing adoption of generative AI.

References

Airoldi, M. (2021). Machine Habitus: Toward a Sociology of Algorithms. Cambridge: Polity.

Allamong, M., Trexler, A., Alqabandi, F., LaChapelle, T., Bail, C.A., Hillygus, D.S., & Volfovsky, A. (2023). Outnumbered Online: The Consequences of Partisan Imbalance in Online Political Discussions. Open Science Framework Preprints. https://doi.org/10.31219/osf.io/tygec

Arora, P., & Natale, S. (2025). Situating AI: Global Media Approaches to Artificial Intelligence. Media, Culture & Society, 47(5), 1007–1011. https://doi.org/10.1177/01634437251341702

Argyle, L.P., Busby, E.C., Fulda, N., Rytting, C., & Wingate, D. (2023). Out of One, Many: Using Language Models to Simulate Human Samples. Political Analysis, 31(3), 337–351. https://doi.org/10.1017/pan.2023.2

Arminio, L., Magnani, M., Piqueras, M., Rossi, L., & Segerberg, A. (2025). Leveraging VLLMs for Visual Clustering: Image-to-Text Mapping Shows Increased Semantic Capabilities and Interpretability. Social Science Computer Review, 44(3), 572–591. https://doi.org/10.1177/08944393251376703

Arnold, T., & Tilton, L. (2019). Distant Viewing: Analyzing Large Visual Corpora. Digital Scholarship in the Humanities, 34(1), 3–16. https://doi.org/10.1093/llc/fqz013

Arseniev-Koehler, A., & Foster, J.G. (2022). Machine Learning as a Model for Cultural Learning: Teaching an Algorithm What it Means to be Fat. Sociological Methods and Research, 51(4), 1484–1539. https://doi.org/10.48550/arXiv.2003.12133

Atil, B., Chittams, A., Fu, L., Ture, F., Xu, L., & Baldwin, B. (2024). LLM Stability: A Detailed Analysis with Some Surprises. https://doi.org/10.48550/arXiv.2408.04667

Atkinson, W. (2025). Artificial Intelligence as a Strategy in the British Economic Field. British Journal of Sociology, 76(4), 814–827. https://doi.org/10.1111/1468-4446.13218

Au, A., & Fong, E. (2025). The Promises and Perils of AI for Sociology. Sociology, 59(6), 1129–1134. https://doi.org/10.1177/00380385251357523

Azar, M., Cox, G., & Impett, L. (2021). Introduction: Ways of Machine Seeing. AI and Society, 36, 1093–1104. https://doi.org/10.1007/s00146-020-01124-6

Baert, P., Dorschel, R., Hall, M., Higgins, I., McPherson, E., & Philip, S. (2025). Dialogues Towards Sociologies of Generative AI. Social Science Computer Review, 44(1), 59–79. https://doi.org/10.1177/08944393251370354

Bail, C.A. (2014). The Cultural Environment: Measuring Culture with Big Data. Theory and Society, 43(3-4), 465–482. https://doi.org/10.1007/s11186-014-9216-5

Bail, C.A. (2024). Can Generative AI Improve Social Science? Proceedings of the National Academy of Sciences of the United States of America, 121(21), e2314021121. https://doi.org/10.1073/pnas.2314021121

Bajohr, H. (2024). Writing at a Distance: Notes on Authorship and Artificial Intelligence. German Studies Review, 47(2), 315–337. https://dx.doi.org/10.1353/gsr.2024.a927862

Barisione, M. (2026). Charismatic Machines: On the Epistemic Power of Generative AI within Platform Convergence. New Media & Society, online first. https://doi.org/10.1177/14614448261441417

Bender, E.M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? FAccT ’21: ACM (Association for Computing Machinery) Conference on Fairness, Accountability, and Transparency, March 2021. https://doi.org/10.1145/3442188.3445922

Bianchi, F., Kalluri, P., Durmus, E., Ladhak, F., Cheng, M., Nozza, D., Hashimoto, T., Jurafsky, D., Zou, J., Caliskan, A. (2023). Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale. FAccT ’23: ACM (Association for Computing Machinery) Conference on Fairness, Accountability, and Transparency, June 2023, Chicago (IL). https://doi.org/10.48550/arXiv.2211.03759

Bommasani, R., Klyman, K., Longpre, S., Kapoor, S., Maslej, N., Xiong, B., Zhang, D., Liang, P. (2023). The Foundation Model Transparency Index. https://doi.org/10.48550/arXiv.2310.12941

Bonikowski, B., & Nelson, L.K. (2022). From Ends to Means: The Promise of Computational Text Analysis for Theoretically Driven Sociological Research. Sociological Methods and Research, 51(4), 1469–1483. https://doi.org/10.1177/00491241221123088

Borch, C. (2023). Machine Learning and Postcolonial Critique: Homologous Challenges to Sociological Notions of Human Agency. Sociology, 57(6), 1450–1466. https://doi.org/10.1177/00380385221146877

Buolamwini, J., & Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Proceedings of Machine Learning Research, 81, 77–91.

Burrell, J., & Fourcade, M. (2021). The Society of Algorithms. Annual Review of Sociology, 47, 213–237. https://doi.org/10.1146/annurev-soc-090820-020800

Calderon, N., Reichart, R., & Dror, R. (2025). The Alternative Annotator Test for LLM-as-a-Judge: How to Statistically Justify Replacing Human Annotators with LLMs. In W. Che, J. Nabende, E. Shutova, & M.T. Pilehvar (Eds.), Annual Meeting of the Association for Computational Linguistics, August 2025, Vienna. https://doi.org/10.48550/arXiv.2501.10970

Cammaerts, B. (2026). Dichotomies in Media and Communication Theory. London: Routledge.

Choenni, S., Busker, T., & Bargh, M.S. (2023). Generating Synthetic Data from Large Language Models. International Conference on Innovations in Information Technology, November 2023, Al Ain. IEEE Computer Society, 2023. https://doi.org/10.1109/IIT59782.2023.10366424

Chopra, F., & Haaland, I. (2023). Conducting Qualitative Interviews with AI. CESifo Working Paper No. 10666. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4583756

Clement, T.E. (2013). Distant Listening or Playing Visualisations Pleasantly with the Eyes and Ears. Digital Studies/Le champ numérique, 3(2). https://doi.org/10.16995/dscn.236

Clement, T.E. (2020). Distant Listening and Resonance. ESC: English Studies in Canada, 46(2), 279–284. https://doi.org/10.1353/esc.2020.a903548

Collett, C. (2025). The Impact of Generative AI on the Novel. Minderoo Centre for Technology and Democracy. https://www.crassh.cam.ac.uk/research/publications/the-impact-of-generative-ai-on-the-novel/

Condorelli, V., Beluzzi, F., & Anselmi, G. (2024). Assessing ChatGPT Political Bias in Italian Language: A Systematic Approach. Comunicazione Politica, 25(3), 409–428. https://doi.org/10.3270/115248

Cowley, S. (2025). Doing Agency: How Agents Adapt in Wide Systems. AI & Society, 40, 1–3. https://doi.org/10.1007/s00146-024-02176-8

Crawford, K. (2021). The Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. New Haven, CT: Yale University Press.

Danziger, R., Levy-Landesberg, H., Yadlin, A., Ramati, I., Lidor, I., & Manor, I. (2026). Not Entirely Artificial, Not That Intelligent. AI and Communication Research: A Conversation on Hype, Contexts, and Practices (Part 1). International Journal of Communication, 20. https://doi.org/10.65476/8tntk984

Dattathrani, S., & De’, R. (2023). The Concept of Agency in the Era of Artificial Intelligence: Dimensions and Degrees. Information Systems Frontiers, 25, 29–54. https://doi.org/10.1007/s10796-022-10336-8

Davidson, T. (2019). Black-box Models and Sociological Explanations: Predicting High School Grade Point Average Using Neural Networks. Socius. Sociological Research for a Dynamic World, 5. https://doi.org/10.1177/2378023118817702

Davidson, T. (2024). Start Generating: Harnessing Generative Artificial Intelligence for Sociological Research. Socius. Sociological Research for a Dynamic World, 10. https://doi.org/10.1177/23780231241259651

Davidson, T., & Karell, D. (2025). Integrating Generative Artificial Intelligence into Social Science Research: Measurement, Prompting, and Simulation. Sociological Methods and Research, 54(3), 775–793. https://doi.org/10.1177/00491241251339184

Davis, J.L., & Sloane, M. (2025). AI’s Sociological Era. Social Science Computer Review, 44(1), 3–9. https://doi.org/10.1177/08944393251394690

Depounti, I., & Natale, S. (2025). Decoding Artificial Sociality: Technologies, Dynamics, Implications. New Media & Society, 27(10), 5457–5470. https://doi.org/10.1177/14614448251359217

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In J. Burstein, C. Doran, & T. Solorio (Eds.), Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, June 2019, Minneapolis (MN). https://doi.org/10.18653/v1/N19-1423

Dunivin, Z.O. (2025). Scaling Hermeneutics: A Guide to Qualitative Coding with LLMs for Reflexive Content Analysis. EPJ Data Science, 14(28). https://doi.org/10.1140/epjds/s13688-025-00548-8

Edelmann, A., Wolff, T., Montagne, D., & Bail, C.A. (2020). Computational Social Science and Sociology. Annual Review of Sociology, 46(1), 61–81. https://doi.org/10.1146/annurev-soc-121919-054621

Egger, R., & Yu, J. (2022). A Topic Modeling Comparison between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts. Frontiers in Sociology, 7. https://doi.org/10.3389/fsoc.2022.886498

Elliott, A. (2022). Making Sense of AI: Our Algorithmic World. Cambridge: Polity.

Emirbayer, M., & Mische, A. (1998). What Is Agency? American Journal of Sociology, 103(4), 962–1023. https://doi.org/10.1086/231294

Esposito, E. (2017). Algorithmic Memory and the Right to be Forgotten on the Web. Big Data & Society, 4(1). https://doi.org/10.1177/2053951717703996

Esposito, E. (2022). Artificial Communication: How Algorithms Produce Social Intelligence. Cambridge, MA: MIT Press.

Esposito, E. (2026). Answer Engines and Other Communication Partners. Communication Theory, 36(2), 86–94. https://doi.org/10.1093/ct/qtaf036

Eubanks, V. (2018). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. New York, NY: Picador.

Evans, J.A. (2022). From Text Signals to Simulations: A Review and Complement to Text as Data by Grimmer, Roberts & Stewart (PUP 2022). Sociological Methods and Research, 51(4), 1868–1885. https://doi.org/10.1177/00491241221123086

Evans, J.A., & Aceves, P. (2016). Machine Translation: Mining Text for Social Theory. Annual Review of Sociology, 42(1), 21–50. https://doi.org/10.1146/annurev-soc-081715-074206

Farooq, A., & de Vreese, C. (2025). (Generative) AI and Disinformation: Introduction. International Journal of Communication, 19, 3559–3576.

Fazi, M.B. (2024). The Computational Search for Unity: Synthesis in Generative AI. Journal of Continental Philosophy, 5(1), 31–56. https://doi.org/10.5840/jcp202411652

Feng, S., Park, C.Y., Liu, Y., & Tsvetkov, Y. From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models. In A. Rogers, J. Boyd-Graber & N. Okazaki (Eds.), Annual Meeting of the Association for Computational Linguistics, July 2023, Toronto. https://aclanthology.org/2023.acl-long.656/

Floridi, L. (2025a). AI as Agency without Intelligence: On Artificial Intelligence as a New Form of Artificial Agency and the Multiple Realisability of Agency Thesis. Philosophy and Technology, 38(30). https://doi.org/10.1007/s13347-025-00858-9

Floridi, L. (2025b). Distant Writing: Literary Production in the Age of Artificial Intelligence. Minds and Machines, 35(30). https://doi.org/10.1007/s11023-025-09732-1

Garzonio, E., Huang, C., & Liberatore, T. (2024). Concept or Language: What is Lost in AI Image Generation? It’s Giving AI: Exploring and Investigating Generative AI Aesthetics. August 5. https://www.digitalmethods.net/Dmi/WinterSchool2024ItsGivingAI

Gensburger, S., & Clavert, F. (2024). Is Artificial Intelligence the Future of Collective Memory? Memory Studies Review, 1(2), 195–208. https://doi.org/10.1163/29498902-202400019

Gilardi, F., Alizadeh, M., & Kubli, M. (2023). ChatGPT Outperforms Crowd Workers for Text-annotation Tasks. Proceedings of the National Academy of Sciences, 120(30). https://doi.org/10.1073/pnas.2305016120

Grossmann, I., Feinberg, M., Parker, D.C., Christakis, N.A., Tetlock, P.E., & Cunningham, W.A. (2023). AI and the Transformation of Social Science Research. Science, 380(6650), 1108–1109. https://doi.org/10.1126/science.adi1778

Grub, M.F., & Humprecht, E. (2025). Defining the Role(s) of AI in Disinformation Research: A Systematic Review. International Journal of Communication, 19, 3577–3601.

Gutierrez Lopez, M., & Halford, S. (2025). Explaining Machine Learning Practice: Findings from an Engaged Science and Technology Studies Project. Information, Communication & Society, 28(4), 616–632. https://doi.org/10.1080/1369118X.2024.2400130

Hayes, A.S. (2023). “Conversing” with Qualitative Data: Enhancing Qualitative Sociological Research through Large Language Models (LLMs). Open Science Framework Preprints. https://osf.io/preprints/socarxiv/yms8p

Hepp, A., Bolin, G., Guzman, A., & Loosen, W. (2024). Mediatization and Human-Machine Communication: Trajectories, Discussions, Perspectives. Human-Machine Communication, 7, 7–21. https://doi.org/10.30658/hmc.7.1

Hoskins, A. (2024). AI and Memory. Memory, Mind and Media, 3, e18. https://doi.org/10.1017/mem.2024.16

Hoskins, A. (2026). AI and Collective Memory. Current Opinion in Psychology, 67. https://doi.org/10.1016/j.copsyc.2025.102156

Hughes, E.C. (1971). The Sociological Eye. Selected Papers. Chicago, IL: Aldine-Atherton.

Johnson, S.J., Murty, M.R., & Navakanth, I. (2024). A Detailed Review on Word Embedding Techniques with Emphasis on Word2vec. Multimed Tools Appl, 83, 37979–38007. https://doi.org/10.1007/s11042-023-17007-z

Johnson, E., & Hajisharif, S. (2024). The Intersectional Hallucinations of Synthetic Data. AI & Society, 40, 1575–1577. https://doi.org/10.1007/s00146-024-02017-8

Jowsey, T., Braun, V., Clarke, V., Lupton, D., & Fine, M. (2025). We Reject the Use of Generative Artificial Intelligence for Reflexive Qualitative Research. https://ssrn.com/abstract=5676462

Joyce, K., & Cruz, T.M. (2024). A Sociology of Artificial Intelligence: Inequalities, Power, and Data Justice. Socius. Sociological Research for a Dynamic World, 10. https://doi.org/10.1177/23780231241275393

Karell, D., & Sachs, J. (2023). How Symbols Influence Social Media Discourse: An Embedding Regression Analysis of Trump’s Return to Twitter. Socius. Sociological Research for a Dynamic World, 9. https://doi.org/10.1177/23780231231212108

Kim, J., & Lee, B. (2023). AI-augmented Surveys: Leveraging Large Language Models for Opinion Prediction in Nationally Representative Surveys. https://doi.org/10.48550/arXiv.2305.09620

Kim, S., Jeong, J., Han, J.S., et al. (2024). LLM-Twin: A Generated-persona Approach for Survey Pre-testing. https://doi.org/10.48550/arXiv.2412.03162

Kozlowski, A.C., Taddy, M., & Evans, J.A. (2019). The Geometry of Culture: Analyzing the Meanings of Class through Word Embeddings. American Sociological Review, 84(5), 905–949. https://doi.org/10.1177/0003122419877135

Krause, M. (2013). Recombining Micro/Macro: The Grammar of Theoretical Innovation. European Journal of Social Theory, 16(2), 139–152. https://doi.org/10.1177/1368431012459696

Laba, N. (2024). Engine for the Imagination? Visual Generative Media and the Issue of Representation. Media, Culture & Society, 46(8), 1599–1620. https://doi.org/10.1177/01634437241259950

Law, T., & McCall, L. (2024). Artificial Intelligence Policymaking: An Agenda for Sociological Research. Socius. Sociological Research for a Dynamic World, 10. https://doi.org/10.1177/23780231241261596

Law, T., & Roberto, E. (2025). Generative Multimodal Models for Social Science: An Application with Satellite and Streetscape Imagery. Sociological Methods and Research, 54(3), 889–932. https://doi.org/10.1177/00491241251339673

Lee, F. (2025). Reassembling Agency: Epistemic Practices in the Age of Artificial Intelligence. Sociologisk Forskning, 62(1–2), 43–58. https://doi.org/10.37062/sf.62.27824

Lee, H.-K. (2024). Reflecting on Cultural Labour in the Time of AI. Media, Culture & Society, 46(6), 1312–1323. https://doi.org/10.1177/01634437241254320

Letiche, T., Lissack, M., & Letiche, H. (2025). Distant Imaging: A Six-Phase Framework for AI Co-Creation in Marketing Education. https://doi.org/10.2139/ssrn.5570898

Lindgren, S. (2023). Critical Theory of AI. Cambridge: Polity.

Liu, D.M., & Salganik, M.J. (2019). Successes and Struggles with Computational Reproducibility: Lessons from the Fragile Families Challenge. Socius. Sociological Research for a Dynamic World, 5. https://doi.org/10.1177/2378023119849803

Lyman, A., Hepner, B., Argyle, L.P., Busby, E.C., Gubler, J.R., & Wingate, D. (2025). Balancing Large Language Model Alignment and Algorithmic Fidelity in Social Science Research. Sociological Methods and Research, 54(3), 1110–1155. https://doi.org/10.1177/00491241251342008

Makhortykh, M., Zucker, E.M., Simon, D.J., Bultmann, D., & Ulloa, R. (2023). Shall Androids Dream of Genocides? How Generative AI Can Change the Future of Memorialization of Mass Atrocities. Discover Artificial Intelligence, 3(28). https://doi.org/10.1007/s44163-023-00072-6

Matei, Ș. (2024). Generative Artificial Intelligence and Collective Remembering: The Technological Mediation of Mnemotechnic Values. Journal of Human-Technology Relations, 2(1), pp. 1–22. https://doi.org/10.59490/jhtr.2024.2.7405

Manning, C.D. (2022). Human Language Understanding and Reasoning. Daedalus, 151(2), 127–138. https://doi.org/10.1162/daed_a_01905

Manovich, L. (2020). Cultural Analytics. Cambridge, MA: MIT Press.

Matrella, A., Cavagnuolo, M., & Capozza, V. (2025). The Use of Artificial Intelligence within Social Research: A Classification Proposal. The Lab’s Quarterly, 27(3). https://doi.org/10.13131/unipi/7c8y-gg23

Merrill, S. (2023). Artificial Intelligence and Social Memory: Towards the Cyborgian Remembrance of an Advancing Mnemo-Technic. In S. Lindgren (Ed.), Handbook of Critical Studies of Artificial Intelligence (pp. 173–186). Cheltenham: Edward Elgar.

Merrill, S., Makhortykh, M., Mandolessi, S., Richardson-Walden, V.G., Smit, R., & Wang, Q. (2025). Handling the Hype: Demystifying Artificial Intelligence for Memory Studies. Memory, Mind and Media, 4(18). https://doi.org/10.1017/mem.2025.10018

Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. https://doi.org/10.48550/arXiv.1301.3781

Mills, C.W. (1959). The Sociological Imagination. Oxford: Oxford University Press.

Molina, M., & Garip, F. (2019). Machine Learning for Sociology. Annual Review of Sociology, 45, 27–45. https://doi.org/10.1146/annurev-soc-073117-041106

Moretti, F. (2013). Distant Reading. London: Verso.

Motoki, F., Neto, V.P., & Rodrigues, V. (2023). More Human than Human: Measuring ChatGPT Political Bias. Public Choice, 198, 3–23. https://doi.org/10.1007/s11127-023-01097-2

Mueller, D.N. (2009). Clouds, Graphs, and Maps: Distant Reading and Disciplinary Imagination [Doctoral dissertation, Syracuse University]. https://surface.syr.edu/wp_etd/19/

Nadeem, A., Marjanovic, O., & Abedin, B. (2022). Gender Bias in AI-based Decision-Making Systems: A Systematic Literature Review. Australasian Journal of Information Systems, 26, 1–34. https://doi.org/10.3127/ajis.v26i0.3835

Natale, S., & Guzman, A.L. (2022). Reclaiming the Human in Machine Cultures: Introduction. Media, Culture & Society, 44(4), 627–637. https://doi.org/10.1177/01634437221099614

Natale, S., & Ji, D. (2025). Human-Machine Communication Cultures: Introduction. Global Media and China, 10(4), 417–424. https://doi.org/10.1177/20594364251353382

Neff, G., & Nagy, P. (2025). The Quasi-Domestication of Social Chatbots: The Case of Replika. New Media & Society, 27(10), 5508–5524. https://doi.org/10.1177/14614448251359218

Nelson, L.K. (2020). Computational Grounded Theory: A Methodological Framework. Sociological Methods and Research, 49(1), 3–42. https://doi.org/10.1177/0049124117729703

Niederer, S., & Colombo, C. (2024). Visual Methods for Digital Research. Cambridge: Polity.

Novelli, E., Ruggiero, C., & Solaroli, M. (2025). Visual Political Communication of Competing Leadership: Italy’s 2024 European Election Campaign on Social Media. Media and Communication, 13. https://doi.org/10.17645/mac.10751

Noble, S.U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. New York, NY: New York University Press.

Ofosu-Ampong, K. (2024). Artificial Intelligence Research: A Review on Dominant Themes, Methods, Frameworks and Future Research Directions. Telematics and Informatics Reports, 14. https://doi.org/10.1016/j.teler.2024.100127

Olivos, F., & Liu, M. (2024). ChatGPTest: Opportunities and Cautionary Tales of Utilizing AI for Questionnaire Pretesting. Field Methods, 37(4), 277–290. https://doi.org/10.1177/1525822X2412805

Olsson, C., Elhage, N., Nanda, N., Joseph, N., DasSarma, N., Henighan, T., Mann, B., Askell, A., Bai, Y., Chen, A., Conerly, T., Drain, D., Ganguli, D., Hatfield-Dodds, Z., Hernandez, D., Johnston, S., Jones, A., Kernion, J., Lovitt, L., Ndousse, K., Amodei, D., Brown, T., Clark, J., Kaplan, J., McCandlish, S., Olah, C. (2022). In-context Learning and Induction Heads. Transformer Circuits Thread, March 8. https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/

Omena, J.J., Autuori, A., Leite Vasconcelos, E., Subet, M., & Botta, M. (2024). AI Methodology Map: Practical and Theoretical Approach to Engage with GenAI for Digital Methods Research. Sociologica, 18(2), 109–144. https://doi.org/10.6092/issn.1971-8853/19566

Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C.L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., & Lowe, R. (2022). Training Language Models to Follow Instructions with Human Feedback. https://doi.org/10.48550/arXiv.2203.02155

Park, J.S., O’Brien, J.C., Cai, C.J., Morris, M.R., Liang, P., & Bernstein, M.S. (2023). Generative Agents: Interactive Simulacra of Human Behavior. In S. Follmer, J. Han, J. Steimle, N. Henry Riche (Eds.), UIST’23: Annual ACM (Association for Computing Machinery) Symposium on User Interface Software and Technology, October 2023, San Francisco (CA). https://doi.org/10.48550/arXiv.2304.03442

Pasquale, F. (2020). New Laws of Robotics: Defending Human Expertise in the Age of AI. Cambridge, MA: Harvard University Press.

Pasquinelli, M., Alaimo, C., & Gandini, A. (2024). AI at Work: Automation, Distributed Cognition, and Cultural Embeddedness. Tecnoscienza – Italian Journal of Science and Technology Studies, 15(1), 99–131. https://doi.org/10.6092/issn.2038-3460/20010

Perkins, M., & Roe, J. (2024). Generative AI Tools in Academic Research: Applications and Implications for Qualitative and Quantitative Research Methodologies. https://doi.org/10.48550/arXiv.2408.06872

Pilati, F., Munk, A.K., & Venturini, T. (2024). Generative AI for Social Research: Going Native with Artificial Intelligence. Sociologica, 18(2), 1–8. https://doi.org/10.6092/issn.1971-8853/20378

Pilati, F. (2025). Sociologia Digitale 2.0. Intelligenza artificiale generativa come oggetto e strumento della ricerca sociale. Milano: Ledizioni.

Pilati, F., & Venturini, T. (2025). The Use of Artificial Intelligence in Counter-Disinformation: A World Wide (Web) Mapping. Frontiers in Political Science, 7. https://doi.org/10.3389/fpos.2025.1517726

Pink, S. (2025). Artificial Intelligence and the Futures Turn: An Anticipatory Infrastructure for Qualitative Methods. Qualitative Research in Psychology, 1–20. https://doi.org/10.1080/14780887.2025.2570167

Poell, T. (2025). Three Challenges for Media and Communication Studies in the Age of AI. Global Media and China, 10(4), 526–533. https://doi.org/10.1177/20594364251400619

Presner, T. (2016). The Ethics of the Algorithm: Close and Distance Listening in the Shoah Foundation Visual History Archive. In C. Fogu, W. Kansteiner, & T. Presner (Eds.), Probing the Ethics of Holocaust Culture (pp. 175–202). Cambridge, MA: Harvard University Press.

Presner, T. (2024). Ethics of the Algorithm. Digital Humanities and Holocaust Memory. Princeton, NJ: Princeton University Press.

Pronzato, R., & Risi, E. (2025). Research on and through Generative AI? An Inevitable Entanglement. The Lab’s Quarterly, 27(3). https://doi.org/10.13131/unipi/7b2k-yp40

Punziano, G. (2025). Adaptive Epistemology: Embracing Generative AI as a Paradigm Shift in Social Science. Societies, 15(7), 205. https://doi.org/10.3390/soc15070205

Rama, I., & Airoldi, M. (2025). The Sociocultural Roots of Artificial Conversations: The Taste, Class and Habitus of Generative AI Chatbots. New Media & Society, 27(10), 5546–5567. https://doi.org/10.1177/14614448251338273

Richardson-Walden, V. G., & Makhortykh, M. (2024). Imagining Human-AI Memory Symbiosis: How Re-Remembering the History of Artificial Intelligence Can Inform the Future of Collective Memory. Memory Studies Review, 1(2), 323–342. https://doi.org/10.1163/29498902-202400016

Rogers, R. (2013). Digital Methods. Cambridge, MA: MIT Press.

Roland, E., So, R., & Long, H. (2025). The Social AI Author: Modeling Creativity and Distinction in Simulated Cultural Fields. AI & Society, 41, 4333–4347. https://doi.org/10.1007/s00146-025-02790-0

Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2021). High-resolution Image Synthesis with Latent Diffusion Models. CVPR: Conference on Computer Vision and Pattern Recognition, June 2021, New Orleans (LA), IEEE/CVF, 2022. https://doi.org/10.48550/arXiv.2112.10752

Rossi, L., Harrison, K., & Shklovski, I. (2024). The Problems of LLM-generated Data in Social Science Research. Sociologica, 18(2), 145–168. https://doi.org/10.6092/issn.1971-8853/19576

Salah, M., Abdelfattah, F., Al Halbusi, H., Jassem, S., Mohammed, M., Ismail, M.M., & Al Balghouni, A. (2024). Can Generative AI Craft Scale Items? A Mixed-Method Study on AI’s Capability to Adapt and Create New Scales with Recommendations for Best Practices. Social Sciences & Humanities Open, 12. https://doi.org/10.1016/j.ssaho.2025.101698

Salganik, M.J. (2017). Bit by Bit: Social Research in the Digital Age. Princeton, NJ: Princeton University Press.

Schneider, M., & Hagendorff, T. (2025). Investigating Toxicity and Bias in Stable Diffusion Text-to-Image Models. Scientific Reports, 15. https://doi.org/10.1038/s41598-025-12032-4

Schroeder, H., Roy, D., & Kabbara, J. (2025). Just Put a Human in the Loop? Investigating LLM-Assisted Annotation for Subjective Tasks. In W. Che, J. Nabende, E. Shutova, & M.T. Pilehvar (Eds.), Annual Meeting of the Association for Computational Linguistics, August 2025, Vienna. https://doi.org/10.18653/v1/2025.findings-acl.1323

Schuh, J. (2024). AI as Artificial Memory: A Global Reconfiguration of Our Collective Memory Practices? Memory Studies Review, 1(2), 231–255. https://doi.org/10.1163/29498902-202400012

Schulz-Schaeffer, I. (2025). Why Generative AI is Different from Designed Technology Regarding Task-relatedness, User Interaction, and Agency. Big Data & Society, 12(3). https://doi.org/10.1177/20539517251367452

Shur-Ofry, M., & Pessach, G. (2020). Robotic Collective Memory. Washington University Law Review, 97(3), 975–1005. https://doi.org/10.2139/ssrn.3364008

Smit, R., Smits, T., & Merrill, S. (2024). Stochastic Remembering and Distributed Mnemonic Agency. Recalling Twentieth Century Activists with ChatGPT, Memory Studies Review, 1(2), 209–230. https://doi.org/10.1163/29498902-202400015

Smits, T., Warner, B., Fyfe, P., & Lee, B.C.G. (2025). A Fully-Searchable Multimodal Dataset of the Illustrated London News, 1842–1890. Journal of Open Humanities Data, 11(1). https://doi.org/10.5334/johd.284

Stark, D. (2022). Questioning Humans versus Machines: Artificial Intelligence in Class Conflict. Administrative Science Quarterly, 67(3), 42–46. https://doi.org/10.1177/00018392221095682

Stark, D., & Vanden Broeck, P. (2024). Principles of Algorithmic Management. Organization Theory, 5(2). https://doi.org/10.1177/26317877241257213

Stoltz, D.S., & Taylor, M.A. (2019). Concept Mover’s Distance: Measuring Concept Engagement via Word Embeddings in Texts. Journal of Computational Social Science, 2, 293–313. https://doi.org/10.1007/s42001-019-00048-6

Symons, J., & Abumusab, S. (2024). Social Agency for Artifacts: Chatbots and the Ethics of Artificial Intelligence. Digital Society, 3(2). https://doi.org/10.1007/s44206-023-00086-8

Than, N., Fan, L., Law, T., Nelson, L.K., & McCall, L. (2025). Updating “the Future of Coding”: Qualitative Coding with Generative Large Language Models. Sociological Methods and Research, 54(3), 849–888. https://doi.org/10.1177/00491241251339188

Törnberg, P. (2023). How to Use LLMs for Text Analysis. https://doi.org/10.48550/arXiv.2307.13106

Törnberg, P. (2024). Best Practices for Text Annotation with Large Language Models. Sociologica, 18(2), 67–85. https://doi.org/10.6092/issn.1971-8853/19461

Tubaro, P. (2025). Sociology of AI, Sociology with AI (1). Data Big and Small, November 16. https://databigandsmall.com/2025/11/16/sociology-of-ai-sociology-with-ai-1/

Tubaro, P., Casilli, A.A., & Coville, M. (2020). The Trainer, the Verifier, the Imitator: Three Ways in Which Human Platform Workers Support Artificial Intelligence. Big Data & Society, 7(1). https://doi.org/10.1177/2053951720919776

Vallejo Vera, S., & Driggers, H. (2025). Bias in LLMs as Annotators: The Effect of Party Cues on Labelling Decisions by Large Language Models. Humanities and Social Sciences Communications, 12. https://doi.org/10.48550/arXiv.2408.15895

Venturini, T. (2023). Bruno Latour and Artificial Intelligence. Tecnoscienza – Italian Journal of Science and Technology Studies, 14(2), 101–114. https://doi.org/10.6092/issn.2038-3460/18359

Venturini, T. (2026). The Vanishing Micro/Macro Divide and the Politics of Computational Interactionism. In A. Koed Madsen & A.K. Munk (Eds.), Handbook of Digital and Computational SSH. Cheltenham: Edward Elgar.

Venturini, T., & Latour, B. (2010). The Social Fabric: Digital Traces and Quali-quantitative Methods. In Proceedings of Future en Seine 2009 (pp. 87–101). Paris: Editions Future en Seine.

Venturini, T., & Rogers, R. (2025). Digital Methods: A Short Introduction. Cambridge: Polity.

Vicinanza, P., Goldberg, A., & Srivastava, S.B. (2023). A Deep-learning Model of Prescient Ideas Demonstrates that They Emerge from the Periphery. PNAS Nexus, 2(1). https://doi.org/10.1093/pnasnexus/pgac275

Wei, J., Bosma, M., Zhao, V.Y., Guu, K., Yu, A.W., Lester, B., Du, N., Dai, A.M., & Le, Q.V. (2022). Finetuned Language Models are Zero-shot Learners. https://doi.org/10.48550/arXiv.2109.01652

Ziems, C., Held, W., Shaikh, O., Chen, J., Zhang, Z., & Yang, D. (2024). Can Large Language Models Transform Computational Social Science? Computational Linguistics, 50(1), 237–291. https://doi.org/10.1162/coli_a_00502

A Sociological Eye on AI. Key Concepts, Analytical Dimensions, and Methodological Tensions

Authors

DOI:

Keywords:

Abstract

References

Downloads

Published

How to Cite

Issue

Section

License

Information

Make a Submission

Keywords

Current Issue