Is Openness in Decline? Data Sharing Between AI Commons, and Predatory Capture

Open Science is meant to make research more transparent, collaborative, and equitable. But with the rise of machine learning and generative AI, new challenges around data sharing have emerged. AI development often depends on publicly shared or scraped data, yet the resulting models and infrastructures are typically closed and corporate-controlled. Thus, researchers increasingly express concern that open data are exploited in ways that strip context, reinforce bias, and concentrate power in proprietary AI systems (Birhane et al., 2022; Jernite et al. 2022, ; Widder et al., 2023; Zueger et al. 2023). In my own research, I have observed growing caution among researchers who once advocated openness. This is not just about lack of incentives but reflects deeper unease with how data circulates and is reused and often misused in AI-driven, commercialized environments. This moderated discussion session aims to learn from researchers and Open Science practitioners about how they experience these tensions. After a 10-minute introduction framing the issues, participants will discuss three guiding questions in three 15-minute rounds, sharing perspectives, dilemmas, and ideas. Contributions via live polls and an online board will help document collective insights and potential paths forward. Birhane, A., Kalluri, P., Card, D., Agnew, W., Dotan, R., & Bao, M. (2022). The values encoded in machine learning research. arXiv preprint arXiv:2106.15590. https://doi.org/10.48550/arXiv.2106.15590 Jernite, Y., Nguyen, H., Biderman, S., Rogers, A., Masoud, M., Danchev, V., Tan, S., Luccioni, A. S., Subramani, N., Johnson, I., Dupont, G., Dodge, J., Lo, K., Talat, Z., Radev, D., Gokaslan, A., Nikpoor, S., Henderson, P., Bommasani, R., & Mitchell, M. (2022). Data governance in the age of large-scale data-driven language technology. Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, 2206–2222. https://doi.org/10.1145/3531146.3534637 Widder, D. G., West, S., & Whittaker, M. (2023). Open (For Business): Big Tech, concentrated power, and the political economy of Open AI. SSRN Scholarly Paper 4543807. https://doi.org/10.2139/ssrn.4543807 Züger, T., Asghari, H. AI for the public. How public interest theory shifts the discourse on AI. AI & Soc 38, 815–828 (2023). https://doi.org/10.1007/s00146-022-01480-5