INTRODUCTION: THE POLITICS AND PRACTICES OF COMPUTATIONAL SEEING

Bibliography

• Agostinho, D. “Chroma Key Dreams: Algorithmic Visibility, Fleshy Images and Scenes of Recognition.” Philosophy of Photography 9, no. 2 (2018): 131–155. doi:10.1386/pop.9.2.131_1.
   Web of Science ®Google Scholar
• Akten, M., Fiebrink, R., and M., Grierson. “Learning to See: You are What You See.” Acm Siggraph 13 (2019): 1–6.
   Google Scholar
• Andrejevic, M., and N. Selwyn. Facial Recognition Cambridge: Polity, 2022.
   Google Scholar
• Bate, D. “The Digital Condition of Photography: Cameras, Computers, and Display.” In The Photographic Image in Digital Culture, edited by Martin Lister, 77–94. 2nd ed. London: Routledge, 2013.
   Google Scholar
• Beer, D. The Data Gaze. London: Sage, 2019.
   Google Scholar
• Benjamin, R. Race After Technology. Cambridge: Polity, 2019.
   Google Scholar
• Blas, Z. “Escaping the Face: Biometric Facial Recognition and the Facial Weaponization Suite.” Media-N 9, no. 2 (2013). https://median.newmediacaucus.org/caa-conference-edition-2013/escaping-the-face-biometric-facial-recognition-and-the-facial-weaponization-suite/
   Google Scholar
• Browne, S. Dark Matters: On the Surveillance of Blackness. Durham: Duke University Press, 2015.
   Google Scholar
• Bucher, T. “Want to Be on the Top? Algorithmic Power and the Threat of Invisibility on Facebook.” New Media & Society 14, no. 7 (2012): 1164–1180. doi:10.1177/1461444812440159.
   Web of Science ®Google Scholar
• Catanzariti, B. “Facial Recognition and the Right to Appear: Infrastructural Challenges in Anti-Surveillance Resistance.” In Data Justice and the Right to the City, edited by Morgan Currie, et al., 282–300. Edinburgh: Edinburgh University Press, 2022.
   Google Scholar
• Chamayou, G. A Theory of the Drone. New York: The New Press, 2015.
   Google Scholar
• Chen, M., A. Radford, R. Child, J. Wu, H. Jun, D. Luan, and I. Sutskever. “Generative Pretraining from Pixels.” Proceedings of the 37th International Conferences on Machine Learning, 1691–1703, Vienna, Austria, 2020.
   Google Scholar
• Crawford, K. The Atlas of A.I. New Haven, CT: Yale University Press, 2021.
   Google Scholar
• Crawford, K., and T. Paglen. “Correction To: Excavating AI: The Politics of Images in Machine Learning Training Sets.” AI & Society 36, no. 4 (2021): 1105–1116. doi:10.1007/s00146-021-01301-1.
   Web of Science ®Google Scholar
• Denton, E., A. Hanna, R. Amironesei, A. Smart, and H. Nicole. “On the Genealogy of Machine Learning Datasets: A Critical History of ImageNet.” Big Data & Society 8, no. 2 (2021): 1–14. doi:10.1177/20539517211035955.
   Web of Science ®Google Scholar
• de Vries, P., and W. Schinkel. “Algorithmic Anxiety: Masks and Camouflage in Artistic Imaginaries of Facial Recognition Algorithms.” Big Data & Society 6, no. 1 (2019): 205395171985153. doi:10.1177/2053951719851532.
   Web of Science ®Google Scholar
• Dewdney, A. Forget Photography. London: Goldsmiths University Press, 2021.
   Google Scholar
• Dewdney, A., and K. Sluis, eds. The Networked Image in Post-Digital Culture. Abingdon: Routledge, 2023.
   Google Scholar
• Dixit, P. “Meet the Three Artists Behind a Landmark Lawsuit Against AI Art Generators.” Buzzfeed News. 2023. https://www.buzzfeednews.com/article/pranavdixit/ai-art-generators-lawsuit-stable-diffusion-midjourney
   Google Scholar
• Dulhanty, C., and A. Wong. “Investigating the Impact of Inclusion in Face Recognition Training Data on Individual Face Identification.” Journal of Computational Vision and Imaging Systems 5, no. 1 (2020): 1–7.
   Google Scholar
• François, C., and I. Robertson. “#operationffs: Fake Face Swarm: A Joint Report by Graphika & the Atlantic Council’s Digital Forensics Lab.” https://www.readkong.com/page/operationffs-fake-face-swarm-7608242
   Google Scholar
• Fussey, P., B. Davies, and M. Innes. “‘Assisted’ Facial Recognition and the Reinvention of Suspicion and Discretion in Digital Policing.” The British Journal of Criminology 61, no. 2 (2021): 325–344. doi:10.1093/bjc/azaa068.
   Web of Science ®Google Scholar
• Geboers, M. A., and C. Thomas Van De Wiele. “Machine Vision and Social Media Images: Why Hashtags Matter.” Social Media + Society 6, no. 2 (2020): 1–15. doi:10.1177/2056305120928485.
   Web of Science ®Google Scholar
• Gebru, T., J. Morgenstern, B. Vecchione, J W. Vaughan, H. Wallach, H D. Iii, K. Crawford. “Datasheets for Datasets.” Communications of the ACM 64, no. 12 (2021): 86–92. doi:10.1145/3458723.
   Google Scholar
• Gehl, R. W., L. Moyer-Horner, and S. K. Yeo. “Training Computers to See Internet Pornography: Gender and Sexual Discrimination in Computer Vision Science.” Television & New Media 18, no. 6 (2017): 529–547. doi:10.1177/1527476416680453.
   Web of Science ®Google Scholar
• Gerling, W. “Photography in the Digital.” Photographies 11, no. 2–3 (2018): 149–167. doi:10.1080/17540763.2018.1445013.
   Google Scholar
• Goldstein, J A., and R. DiResta. “Research Note: This Salesperson Does Not Exist: How Tactics from Political Influence Operations on Social Media are Deployed for Commercial Lead Generation.” Harvard Kennedy School Misinformation Review 3, no. 5 (2022): 1–15. doi:10.37016/mr-2020-104.
   Google Scholar
• Gómez Cruz, E. “Photo-Genic Assemblages: Photography as a Connective Interface.” In Digital Photography and Everyday Life, edited by E. Gómez Cruz and A. Lehmuskallio, 228–242. London: Routledge, 2016.
   Google Scholar
• Gómez Cruz, E., and E.T. Meyer. “Creation and Control in the Photographic Process: iPhones and the Emerging Fifth Moment of Photography.” Photographies 5, no. 2 (2012): 203–221. doi:10.1080/17540763.2012.702123.
   Google Scholar
• Halpern, O. “Perceptual Machines: Communication, Archiving, and Vision in Post-War American Design.” Journal of Visual Culture 11, no. 3 (2012): 328–351. doi:10.1177/1470412912455619.
   Web of Science ®Google Scholar
• Hand, M. “Photography Meets Social Media: Image Making and Sharing in a Continually Networked Present.” In The Handbook of Photography Studies, edited by Gil Pasternak, 310–326, London: Bloomsbury, 2019.
   Google Scholar
• Henning, M. “Image Flow: Photography on Tap.” Photographies 11, no. 2–3 (2018): 133–148. doi:10.1080/17540763.2018.1445011.
   Google Scholar
• Heras, D., and T. Blanke. “On Machine Vision and Photographic Imagination.” AI & Society 36, no. 4 (2021): 1–13. doi:10.1007/s00146-020-01091-y.
   Web of Science ®Google Scholar
• Hoelzl, I., and R. Marie. Softimage. Bristol and Chicago: Intellect, 2015.
   Google Scholar
• Hu, T., and E. Papa. “How AI Manufactures a Smile: Tung-Hui Hu Interviews Artist Elisa Giardina Papa on Digital Labor.” Media-N | the Journal of the New Media Caucus 16, no. 1 (2020): 141–150. doi:10.21900/j.median.v16i1.360.
   Google Scholar
• Jacobsen, B. “Regimes of Recognition on Algorithmic Media.” New Media & Society (2021): 1–16. doi:10.1177/14614448211053555.
   Web of Science ®Google Scholar
• Jurgenson, N. The Social Photo: On Photography and Social Media. London: Verso, 2019.
   Google Scholar
• Kember, S. “Ambient Intelligent Photography.” In The Photographic Image in Digital Culture, edited by M. Lister, 56–76, New York: Routledge, 2013.
   Google Scholar
• Kember, S. “Face Recognition and the Emergence of Smart Photography.” Journal of Visual Culture 13, no. 2 (2014): 182–199. doi:10.1177/1470412914541767.
   Web of Science ®Google Scholar
• Lee, R. “Machine Learning and Notions of the Image.” PhD Thesis, Center for Computer Games Research Department of Digital Design IT, University of Copenhagen, 2020.
   Google Scholar
• Levitt, D. The Animatic Apparatus: Animation, Vitality, and the Futures of the Image. Winchester: Zero Books, 2018.
   Google Scholar
• Mackenzie, A. Machine Learners. Archaeology of a Data Practice. Cambridge, MA: MIT Press, 2017.
   Google Scholar
• MacKenzie, A., and A. Munster. “Platform Seeing: Image Ensembles and Their Invisualities.” Theory, Culture & Society 36, no. 5 (2019): 3–22. doi:10.1177/0263276419847508.
   Web of Science ®Google Scholar
• Malevé, N. “On the Data Set’s Ruins.” AI & Society 36, no. 4 (2021): 1117–1131. doi:10.1007/s00146-020-01093-w.
   Web of Science ®Google Scholar
• Manovich, L. “Automation of Sight: From Photography to Computer Vision,” 1997. http://manovich.net/content/04-projects/014-automation-of-sight-from-photography-to-computer-vision/11_article_1997.pdf
   Google Scholar
• McCosker, A., and R. Wilken. Automating Vision: The Social Impact of the New Camera Consciousness. New York: Routledge, 2020.
   Google Scholar
• Meikle, G. Deepfakes. London: Polity Press, 2022.
   Google Scholar
• Mitchell, W.J.T. The Reconfigured Eye. Cambridge, MA: MIT Press, 1992.
   Google Scholar
• Miyazaki, S. “Take Back the Algorithms! A Media Theory of Commonistic Affordance.” Media Theory: Rethinking Affordance Special Issue 3, no. 1 (2019): 269–286.
   Google Scholar
• Monahan, T. “The Right to Hide? Anti-Surveillance Camouflage and the Aestheticization of Resistance.” Communication and Critical/Cultural Studies 12, no. 2 (2015): 159–178. doi:10.1080/14791420.2015.1006646.
   Web of Science ®Google Scholar
• Mühlhoff, R. “Human-Aided Artificial Intelligence: Or, How to Run Large Computations in Human Brains? Toward a Media Sociology of Machine Learning.” New Media & Society 22, no. 10 (2020): 1868–1884. doi:10.1177/1461444819885334.
   Web of Science ®Google Scholar
• Natale, S., and A. Guzman. “Reclaiming the Human in Machine Cultures: Introduction.” Media, Culture & Society 44, no. 4 (2022): 637. doi:10.1177/01634437221099614.
   Web of Science ®Google Scholar
• Neumayer, C., and L. Rossi. “Images of Protest in Social Media: Struggle Over Visibility and Visual Narratives.” New Media & Society 20, no. 11 (2018): 4293–4310. doi:10.1177/1461444818770602.
   Web of Science ®Google Scholar
• Noble, S.U. Algorithms of Oppression: How Search Engines Reinforce Racism. New York: NYU Press, 2018.
   Google Scholar
• Oppenlaender, J. “The Creativity of Text-To-Image Generation.” Proceedings of the 25th International Academic Mindtrek Conference Tampere, Finland, 2022, 192–202
   Google Scholar
• Parikka, J. “The Diagrams of AI (Image).” The Nordic Journal of Aesthetics 30, no. 61–62 (2021): 61–62. doi:10.7146/nja.v30i61-62.127893.
   Google Scholar
• Qadri, R., and C. D’ignazio. “Seeing Like a Driver: How Workers Repair, Resist, and Reinforce the Platform’s Algorithmic Visions.” Big Data & Society 9, no. 2 (2022): 1–17. doi:10.1177/20539517221133780.
   Web of Science ®Google Scholar
• Ramesh, A., P. Dhariwal, A. Nichol, C. Chu, and M. Chen. “Hierarchical Text-Conditional Image Generation with CLIP Latents.” arXiv (2022). https://arxiv.org/abs/2204.06125
   Google Scholar
• Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Checn, M., and Sutskever, I. “Zero-Shot Text-To-Image Generation.” Proceedings of the 38th International Conference on Machine Learning 139 (2021): 8821–8831.
   Google Scholar
• Rettberg, J.W. “Biometric Citizens: Adapting Our Selfies to Machine Vision.” In Selfie Citizenship, edited by A. Kuntsman, 89–96. London: Palgrave Macmillan, 2017.
   Google Scholar
• Rettberg, J.W. Machine Vision: How Algorithms are Changing the Way We See the World. Cambridge: Polity, 2023.
   Google Scholar
• Roberts, S.T. Behind the Screen: Content Moderation in the Shadows of Social Media. New Haven: Yale University Press, 2021.
   Google Scholar
• Rose, G. “Animated Embodiment: Seeing Bodies in Digitally-Mediated Cities.” In Seeing the City Digitally: Processing Urban Space and Time, edited by Gillian Rose, 139–156. Amsterdam: Amsterdam University Press, 2022.
   Google Scholar
• Routhier, D., L. Lee-Morrison, and K. Maurer. “Automating Visuality: An Introduction” The Journal of Media Art Study and Theory: Special Issue: Automating Visuality 3, no. 1 (2022): 2–14.
   Google Scholar
• Rubenstein, D. “Fractal Photography and the Politics of Invisibility.” In Routledge Companion to Photography Theory, edited by M. Durden and J. Tormey, 337–355. London: Routledge, 2019.
   Google Scholar
• Rubinstein, D. “Value of Nothing.” Journal of Visual Art Practice 15, no. 2–3 (2016): 127–137. doi:10.1080/14702029.2016.1228854.
   Google Scholar
• Rubinstein, D., and K. Sluis. “A Life More Photographic: Mapping the Networked Image.” Photographies 1, no. 1 (2008): 1–28. doi:10.1080/17540760701785842.
   Google Scholar
• Rubinstein, D., and K. Sluis. “The Digital Image in Photographic Culture; the Algorithmic Image and the Crisis of Representation.” In The Photographic Image in Digital Culture, edited by M. Lister, 22–40. 2nd ed. New York: Routledge, 2013.
   Google Scholar
• Satter, R. “Experts: Spy Used AI-Generated Face to Connect with Targets.” Associated Press News. June 13, 2019. https://apnews.com/article/ap-top-news-artificial-intelligence-social-platforms-think-tanks-politics-bc2f19097a4c4fffaa00de6770b8a60d
   Google Scholar
• Scarlett, A. “Interpreting an Improper Materialism: On Aesthesis, Synesthesia and the Digital.” Digital Culture & Society 1, no. 1 (2015): 111–129. doi:10.14361/dcs-2015-0108.
   Google Scholar
• Scheuerman, M., A. Hanna, E. Denton. “Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development.” Proceedings of the ACM on Human-Computer Interaction 5, no. 2 (2021): 1–37. doi:10.1145/3476058.
   PubMedGoogle Scholar
• Sluis, K. “Beyond Representation? The Database-Driven Image and the Non-Human Spectator.” In A Companion to Photography, edited by S. Bull, 113–129. London: Wiley-Blackwell, 2020.
   Google Scholar
• Sobel, B. “A New Common Law of Web Scraping.” Lewis & Clark Law Review 25, no. 1 (2021): 147–208.
   Google Scholar
• Stejskalová, T. “The Surrogate Labor of the Eye: Farocki, Papa, and the Eeefff Collective.” Journal of Aesthetics & Culture 14, no. 1 (2020): 1–12. doi:10.1080/20004214.2022.2156754.
   Web of Science ®Google Scholar
• Szeliski, R. Computer Vision: Algorithms and Applications. London: Sage, 2011.
   Google Scholar
• Taffel, S. “Google’s Lens: Computational Photography and Platform Capitalism.” Media, Culture & Society 43, no. 2 (2020): 237–255. doi:10.1177/0163443720939449.
   Web of Science ®Google Scholar
• Thylstrup, N. “The Ethics and Politics of Data Sets in the Age of Machine Learning: Deleting Traces and Encountering Remains.” Media, Culture & Society 44, no. 4 (2022): 655–671. doi:10.1177/01634437211060226.
   Web of Science ®Google Scholar
• Uliasz, R. “Seeing Like an Algorithm: Operative Images and Emergent Subjects.” AI & Society 36, no. 4 (2022): 1233–1241. doi:10.1007/s00146-020-01067-y.
   Web of Science ®Google Scholar
• Vincent, J. “Getty Images is Suing the Creators of AI Art Tool Stable Diffusion for Scraping Its Contents.” The Verge. 2022. https://www.theverge.com/2023/1/17/23558516/ai-art-copyright-stable-diffusion-getty-images-lawsuit
   Google Scholar
• Zhang, Y. “Algorithmic Photography: A Case Study of the Huawei Moon Mode Controversy.” Media, Culture & Society 44, no. 4 (2020): 690–705. doi:10.1177/01634437211064964.
   Web of Science ®Google Scholar
• Zuboff, S. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. New York: Public Affairs, 2019.
   Google Scholar
• Zuech, N. “Seeing Machine Vision in a Different Light: As Machine Vision Comes of Age, Get to Know This Yield Enhancement Tool and Its Capabilities.” Quality 41, no. 3 (2003).
   Google Scholar
• Zylinksa, J. Nonhuman Photography. Cambridge, MA: MIT Press, 2017.
   Google Scholar