Readings

A short list, not a bibliography. Each one is here for a reason, given in a line.

How these models work

• Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. doi.org/10.1145/3442188.3445922The influential argument that fluent text is not the same as understanding.
• Carlini, N., Paleka, D., Dvijotham, K., Steinke, T., et al. (2024). Stealing part of a production language model. arXiv:2403.06634. arxiv.org/abs/2403.06634Shows that access to a model’s word probabilities reveals facts about its internals, one reason vendors now hide them.

LLMs as research instruments

• 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). doi.org/10.1162/coli_a_00502Tests how well LLMs handle common text classification and explanation tasks in social science, and where they fall short.
• Argyle, L. P., Busby, E. C., Fulda, N., Gubler, J. R., Rytting, C., & Wingate, D. (2023). Out of one, many: Using language models to simulate human samples. Political Analysis. doi.org/10.1017/pan.2023.2Uses a language model to simulate survey respondents from different groups.
• Dillion, D., Tandon, N., Gu, Y., & Gray, K. (2023). Can AI language models replace human participants? Trends in Cognitive Sciences. doi.org/10.1016/j.tics.2023.04.008A short, direct take on when a model can stand in for participants and when it can’t.
• 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. doi.org/10.1126/science.adi1778A broad overview of how LLMs may change social science methods.
• Riemer, K., Peter, S., Schwabe, G., Chatterjee, S., Adam, M., & Davison, R. M. (2025). Generative AI is neither just another IT artefact nor a colleague: Methodological guidance for IS scholarship. Information Systems Journal. doi.org/10.1111/isj.70027Methods guidance built on the point that these models are probabilistic, not deterministic.
• Davison, R. M., Chughtai, H., Nielsen, P., Marabelli, M., Iannacci, F., van Offenbeek, M., et al. (2024). The ethics of using generative AI for qualitative data analysis. Information Systems Journal. doi.org/10.1111/isj.12504What changes, ethically, when a model helps analyze qualitative data.

What journals and editors are saying

• Roberson, Q. M. (2026). Artificial intelligence and responsible research at AMJ. Academy of Management Journal. doi.org/10.5465/amj.2026.4002AMJ’s 2026 rules for authors and reviewers.
• Baer, M. D., & Kouchaki, M. (2026). Responsible collaboration with artificial intelligence in organizational scholarship: OBHDP’s governance framework for authors and reviewers. Organizational Behavior and Human Decision Processes. doi.org/10.1016/j.obhdp.2026.104480A framework that sorts AI uses into categories, with disclosure and sanctions attached.
• Gartenberg, C., Hasan, S., Murray, A., & Pierce, L. (2026). More versus better: Artificial intelligence, incentives, and the emerging crisis in peer review. Organization Science. doi.org/10.1287/orsc.2026.ed.v37.n3Evidence on how AI is changing submission volume, and what that does to peer review.
• Bhargava, H. K., Bana, S. H., Zhang, Z., Brandimarte, L., Choudhary, V., Li, J. F., Loupos, P., & Zantedeschi, D. (2026). Fighting fire with fire: Infusing artificial intelligence into peer review to sustain quality scholarship. Management Science. doi.org/10.1287/mnsc.2026.00184A proposal to bring AI into peer review itself, published with commentaries.
• Holmström, J., & Davison, R. M. (2026). Scholarly integrity and generative AI: Five boundary violations for IS scholarship. Information Systems Journal. doi.org/10.1111/isj.70044Where one journal draws the line, stated as five specific violations.
• Grimes, M., von Krogh, G., Feuerriegel, S., Rink, F., & Gruber, M. (2023). From scarcity to abundance: Scholars and scholarship in an age of generative artificial intelligence. Academy of Management Journal. doi.org/10.5465/amj.2023.4006An early agenda from AMJ’s editors on what generative AI means for scholarship.
• Bechky, B. A., & Davis, G. F. (2024). Resisting the algorithmic management of science: Craft and community after generative AI. Administrative Science Quarterly. doi.org/10.1177/00018392241304403An essay on protecting the craft and community of research.
• Tomaino, G., Cooke, A., & Hoover, J. (2025). AI and the advent of the cyborg behavioral scientist. Journal of Consumer Psychology. doi.org/10.1002/jcpy.1452A provocation about automating behavioral science, published with comments.

Journal AI policies

For what each journal actually allows, in its own words, see the journal policy lookup.

Last updated September 2026.