Synthetic Probes: A Qualitative Experiment in Latent Space Exploration
DOI:
https://doi.org/10.6092/issn.1971-8853/19512Keywords:
Generative artificial intelligence, latent space, machine learning models, probes, qualitative methodsAbstract
This essay outlines a methodological approach for the qualitative study of generative artificial intelligence models. After introducing the epistemological challenges faced by users of generative models, I argue that these black-boxed systems can be explored through indirect ways of knowing what happens inside them. Inspired by both ethnographic and digital methods, I propose the use of what I call synthetic probes: qualitative research devices designed to correlate the inputs and outputs of generative models and thus gather insights into their training data, informational representation, and capability for synthesis. I start by describing the sociotechnical context of a specific text-to-video generative model (ModelScopeT2V), and then explain how my encounter with it resulted in an extensive period of experimentation dedicated to the production of Latent China, a documentary entirely composed of synthetic video clips. Reflecting on how this experience bridges qualitative research and creative practice, I extrapolate more general observations about how a long history of research probes across disciplines can inspire the creation of methodological devices designed to allow the indirect exploration of a machine learning model’s latent space.
References
Alain, G., & Bengio, Y. (2018). Understanding Intermediate Layers Using Linear Classifier Probes (arXiv:1610.01644). arXiv. http://arxiv.org/abs/1610.01644
Alibaba Cloud Community. (2022). Alibaba Cloud Launches ModelScope Platform and New Solutions to Lower the Threshold for Materializing Business Innovation. Alibaba Cloud. https://www.alibabacloud.com/blog/alibaba-cloud-launches-modelscope-platform-and-new-solutions-to-lower-the-threshold-for-materializing-business-innovation_599467
Barr, K. (2023). Text to Video Generative AI is Finally Here and It’s Weird as Hell. Gizmodo. https://gizmodo.com/text-to-video-ai-art-generator-runway-modelscope-ai-1850249431
Boehner, K., Vertesi, J., Sengers, P., & Dourish, P. (2007). How HCI Interprets the Probes. In M.B. Rosson & D.J. Gilmore (Eds.), Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1077–1086). New York, NY: ACM Press. https://doi.org/10.1145/1240624.1240789
chaindrop. (2023). Will Smith Eating Spaghetti [Reddit Post]. R/StableDiffusion. https://www.reddit.com/r/StableDiffusion/comments/1244h2c/will_smith_eating_spaghetti/
Cole, S. (2023). AI Will Smith Eating Spaghetti Will Haunt You For the Rest of Your Life. Vice. https://www.vice.com/en/article/xgw8ek/ai-will-smith-eating-spaghetti-hill-haunt-you-for-the-rest-of-your-life
Colombo, G., Niederer, S., de Gaetano, C., & Borie, M. (2023). Prompting Generative Visual AI for Biodiversity: From Prompt Engineering to Prompt Design. Generative Methods – AI as Collaborator and Companion in the Social Sciences and Humanities. Conference, Aalborg University, December 6–8.
Davison, R.M., Chughtai, H., Nielsen, P., Marabelli, M., Iannacci, F., van Offenbeek, M., Tarafdar, M., Trenz, M., Techatassanasoontorn, A.A., Díaz Andrade, A., & Panteli, N. (2024). The Ethics of Using Generative AI for Qualitative Data Analysis. Information Systems Journal, 34(5), 1433–1439. https://doi.org/10.1111/isj.12504
De Leon, J.P., & Cohen, J.H. (2005). Object and Walking Probes in Ethnographic Interviewing. Field Methods, 17(2), 200–204. https://doi.org/10.1177/1525822X05274733
Denton, E., Hanna, A., Amironesei, R., Smart, A., & Nicole, H. (2021). On the Genealogy of Machine Learning Datasets: A Critical History of ImageNet. Big Data & Society, 8(2), 1–14. https://doi.org/10.1177/20539517211035955
de Seta, G., Pohjonen, M., & Knuutila, A. (2023). Synthetic Ethnography: Field Devices for the Qualitative Study of Generative Models. SocArXiv. https://doi.org/10.31235/osf.io/zvew4
Diakopoulos, N. (2015). Algorithmic Accountability: Journalistic Investigation of Computational Power Structures. Digital Journalism, 3(3), 398–415. https://doi.org/10.1080/21670811.2014.976411
Elish, M.C., & boyd, danah. (2018). Situating Methods in the Magic of Big Data and AI. Communication Monographs, 85(1), 57–80. https://doi.org/10.1080/03637751.2017.1375130
Gan, W., Wan, S., & Yu, P.S. (2023). Model-as-a-Service (MaaS): A survey (arXiv:2311.05804). arXiv. http://arxiv.org/abs/2311.05804
Gaver, B., Dunne, T., & Pacenti, E. (1999). Cultural Probes. Interactions, 6(1), 21–29. https://doi.org/10.1145/291224.291235
Gaver, W.W., Boucher, A., Pennington, S., & Walker, B. (2004). Cultural Probes and the Value of Uncertainty. Interactions, 11(5), 53–56. https://doi.org/10.1145/1015530.1015555
Hemmings, T., Crabtree, A., Rodden, T., Clarke, K., & Rouncefield, M. (2002). Probing the Probes. In T. Binder, J. Gregory & I. Wagner (Eds.), Proceedings of the Participatory Design Conference (pp. 42–50). Palo Alto, CA: CPSR.
Henrickson, L., & Meroño‑Peñuela, A. (2023). Prompting Meaning: A Hermeneutic Approach to Optimising Prompt Engineering with ChatGPT. AI & Society. https://doi.org/10.1007/s00146-023-01752-8
Hoover, A. (2023). AI Videos Are Freaky and Weird Now. But where Are They Headed? WIRED. https://www.wired.com/story/text-to-video-ai-generators-filmmaking-hollywood/
Institute for Intelligent Computing. (2023). 文本生成视频大模型-英文-通用领域 [Text-to-video Synthesis Model—English—Public domain]. ModelScope. https://www.modelscope.cn/models/iic/text-to-video-synthesis
Jiang, J.A., Wade, K., Fiesler, C., & Brubaker, J.R. (2021). Supporting Serendipity: Opportunities and Challenges for Human-AI Collaboration in Qualitative Analysis. In J. Grudin & J. Carroll (Eds.), Proceedings of the ACM on Human-Computer Interaction (pp. 1–23). New York, NY: ACM Press. https://doi.org/10.1145/3449168
MacKenzie, A., & Munster, A. (2019). Platform Seeing: Image Ensembles and Their Invisualities. Theory, Culture & Society, 36(5), 3–22. https://doi.org/10.1177/0263276419847508
Marres, N., & Gerlitz, C. (2016). Interface Methods: Renegotiating Relations between Digital Social Research, STS and Sociology. The Sociological Review, 64(1), 21–46. https://doi.org/10.1111/1467-954X.12314
Mok, A. (2023). I Can’t Stop Watching These Hilariously Bad AI-Generated Videos of Celebrities Like Will Smith and Scarlett Johansson. Business Insider. https://www.businessinsider.com/watch-hilariously-bad-ai-modelscope-videos-will-smith-scarlett-johansson-2023-3
Munk, A.K. (2023). Coming of Age in Stable Diffusion. Anthropology News. https://www.anthropology-news.org/articles/coming-of-age-in-stable-diffusion/
Munn, L., & Henrickson, L. (2024). Tell Me a Story: A Framework for Critically Investigating AI Language Models. Learning, Media and Technology, 1–17. https://doi.org/10.1080/17439884.2024.2327024
Munn, L., Magee, L., & Arora, V. (2023). Unmaking AI Imagemaking: A Methodological Toolkit for Critical Investigation (arXiv:2307.09753). arXiv. http://arxiv.org/abs/2307.09753
Offert, F. (2023). On the Concept of History (in Foundation Models). IMAGE, 37(1), 121–134. https://doi.org/10.1453/1614-0885-1-2023-15462
Pipkin, E. (2020). On Lacework: Watching an Entire Machine-Learning Dataset. Unthinking Photography. https://unthinking.photography/articles/on-lacework
Rogers, R. (2013). Digital Methods. Cambridge, MA: MIT Press.
Salvaggio, E. (2023). How to Read an AI Image: Toward a Media Studies Methodology for The Analysis of Synthetic Images. IMAGE, 37(1), 83–89. https://doi.org/10.1453/1614-0885-1-2023-15456
TechNode Feed. (2023). Alibaba’s ModelScope Attracts Over 2 Million Developers Amid AI Frenzy. TechNode. http://technode.com/2023/08/01/alibabas-modelscope-attracts-over-2-million-developers-amid-ai-frenzy/
Veel, K. (2021). Latency. In N.B. Thylstrup, D. Agostinho, A. Ring, C. D’Ignazio, & K. Veel (Eds.), Uncertain Archives: Critical Keywords for Big Data (pp. 313–319). Cambridge, MA: MIT Press. https://doi.org/10.7551/mitpress/12236.003.0034
Wang, J., Yuan, H., Chen, D., Zhang, Y., Wang, X., & Zhang, S. (2023). ModelScope Text-to-Video Technical Report (arXiv:2308.06571). arXiv. http://arxiv.org/abs/2308.06571
Wang, S.C., Van Durme, B., Eisner, J., & Kedzie, C. (2024). Do Androids Know They’re Only Dreaming of Electric Sheep? (arXiv:2312.17249). arXiv. http://arxiv.org/abs/2312.17249
Wilkie, A., Michael, M., & Plummer-Fernandez, M. (2015). Speculative Method and Twitter: Bots, Energy and Three Conceptual Characters. The Sociological Review, 63(1), 79–101. https://doi.org/10.1111/1467-954X.12168
Will Smith [@WillSmith2real]. (2024). This Is Getting Out of Hand! [Tweet]. X (formerly Twitter). https://twitter.com/WillSmith2real/status/1759703359727300880
Willim, R. (2017). Evoking Imaginaries: Art Probing, Ethnography and More-than-Academic Practice. Sociological Research Online, 22(4), 208–231. https://doi.org/10.1177/1360780417726733
Yu, I. (2023). Q&A: Alibaba Cloud’s CTO on Creating China’s Biggest AI Model Community. Alizila. https://www.alizila.com/alibaba-cloud-cto-creating-china-biggest-ai-model-community-llm/
Ziewitz, M. (2016). Governing Algorithms: Myth, Mess, and Methods. Science, Technology, & Human Values, 41(1), 3–16. https://doi.org/10.1177/0162243915608948
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