Abstract
Algorithmic vision, the computational process of making meaning from digital images or visual information, has changed the relationship between the image and the human subject. In this paper, I explicate on the role of algorithmic vision as a technique of algorithmic governance, the organization of a population by algorithmic means. With its roots in the United States post-war cybernetic sciences, the ontological status of the computational image undergoes a shift, giving way to the hegemonic use of automated facial recognition technologies towards predatory policing and profiling practices. By way of example, I argue that algorithmic vision reconfigures the philosophical links between vision, image, and truth, paradigmatically changing the way a human subject is represented through imagistic data. With algorithmic vision, the relationship between subject and representation challenges the humanistic discourse around images, calling for a critical displacement of the human subject from the center of an analysis of how computational images make meaning. I will explore the relationship between the operative image, the image that acts but is not seen by human eyes, and what Louise Amoore calls an “emergent subject,” a subject that is made visible through algorithmic techniques (2013). Algorithmic vision reveals subjects to power in a mode that requires a new approach towards analyzing the entanglement and invisiblization of the human in automated decision-making systems.
Similar content being viewed by others
Data availability
Not applicable.
Code availability
Not applicable.
Notes
For recent critical and artistic research into the origins of many of the datasets that have become standard for training facial recognition algorithms today see Adam Harvey and Jules LaPlace’s online platform project, Megapixels (megapixels.cc).
References
Agamben G (2005) State of Exception. University of Chicago Press, Chicago
Amoore L (2013) The Politics of Possibility: Risk and Security Beyond Probability. Duke University Press, Durham, NC
Amoore L (2019) Doubt and the algorithm: on the partial accounts of machine learning. Theory Cult Soc 36(6):147–169
Apprich C, Steyerl H, Chun W, Cramer F (2018) Pattern Discrimination. University of Minnesota Press
Barthes R (1981) Camera Lucida: Reflections on Photography. Hill and Wang, New York
Bowker G, Star S (1999) Sorting things out: Classification and its consequences. MIT Press, Cambridge
Browne S (2015) Dark Matters: On the Surveillance of Blackness. Duke University Press, Durham
Buckley C, Mozur P (2019) How China Uses High-Tech Surveillance to Subdue Minorities. New York Times. https://www.nytimes.com/2019/05/22/world/asia/china-surveillance-xinjiang.html. Accessed Dec 2019
Byler D (2019) China’s hi-tech war on its Muslim minority. The guardian. https://www.theguardian.com/news/2019/apr/11/china-hi-tech-war-on-muslim-minority-xinjiang-uighurs-surveillance-face-recognition
Chun W (2018) On patterns and proxies, or the perils of reconstructing the unknown. e-flux Architecture: Accumulation. https://www.e-flux.com/architecture/accumulation/212275/on-patterns-and-proxies/. Accessed Nov 2019
Cooper D (2020) Invisible desert. e-flux Architecture: New Silk Roads. https://www.e-flux.com/architecture/new-silk-roads/313103/invisible-desert/. Accessed Feb 2020
Deleuze G (1988) Foucault. University of Minnesota Press
Farocki H (Director) (2000) I Thought I Was Seeing Convicts [Motion Picture]
Farocki H (2004) Phantom Images. PUBLIC 29:12–22
Flusser V (2000) Towards a Philosophy of Photography. Reaktion Books
Foucault M (1995) Panopticism. Discipline and Punish: the Birth of the Prison. Vintage Books, New York, pp 195–230
Foucault M (2008) The Birth of Biopolitics. Palgrave Macmillan, New York
Foucault M (2009) Security, Territory, Population. Palgrave Macmillan, London
Franklin S (2015) Control: Digitality as Cultural Logic. MIT Press, Cambridge
Goodfellow I, Bengio Y, Courville A (2016) Deep Learning. MIT Press
Halpern O (2015) Beautiful Data: A History of Vision and Reason since 1945. Duke University Press, Durham
Hansen MBN (2014) Feed-Forward: On the Future of Twenty-First-Century Media. University of Chicago Press, Chicago
Harvey A, LaPlace J (2019) DukeMTMC. MegaPixels: origins, ethics, and privacy implications of publicly available face recognition image datasets. https://megapixels.cc/. Accessed Dec 2019
Hoel A (2018) Operative Images. Inroads to a New Paradigm of Media Theory. Image – Action – Space. De Gruyter, Berlin, Boston. https://doi.org/10.1515/9783110464979-002
Hoelzl I, Marie R (2015) Softimage: Towards a New Theory of the Digital Image. Intellect Ltd, Chicago
Human Rights Watch (2019) China’s algorithms of repression: reverse engineering a Xinjiang Police Mass Surveillance App. Human rights watch. https://hrw.org. Accessed Dec 2019
Lettvin JY, Maturana HR, McCulloch WS, Pitts WH (1959) What the frog’s eye tells the frogs brain. Proc IRE 47(11):1940–1951
Lyon D (2007) Surveillance, security and social sorting. Int Crim Justice Rev 17(3):161–170
MacKenzie A, Munster A (2019) Platform seeing: image ensembles and their invisualities. Theor Cult Soc 36(5):3–22. https://doi.org/10.1177/0263276419847508
Mirowski P (2002) Machine Dreams: Economics Becomes a Cyborg Science. Cambridge University Press, Cambridge
Murgia M (2019) Who’s using your face? The ugly truth about facial recognition. The financial times. https://www.ft.com/content/cf19b956-60a2-11e9-b285-3acd5d43599e. Accessed Sept 2019
Ningning Z (2016) Big data “out of traffic”. Southern Magazine. https://epaper.southcn.com. Accessed Sept 2019
O'Neil C (2016) Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown, New York
Ong A (2006) Neoliberalism as Exception: Mutations in Citizenship and Sovereignty. Duke University Press, Durham
Paglen T (2016) Invisible Images (Your Pictures Are Looking at You). The New Inquiry. https://thenewinquiry.com/invisible-images-your-pictures-are-looking-at-you/. Accessed May 2019
Paglen T, Crawford K (2019) Excavating AI The Politics of Images in Machine Learning Training Sets. https://excavating.ai. Accessed Sept 2019
Parisi L (2013) Contagious architecture: Computation, aesthetics, and space. MIT Press, Cambridge
Pasquinelli M (2015) Anomaly detection: the mathematization of the abnormal in the metadata society. transmediale. Berlin
Pasquinelli M (2019) How a machine learns and fails: a grammar of error for artificial intelligence. Spheres, vol 5
Patterson Z (2015) Peripheral Vision: Bell Labs, the S-C 4020, and the Origins of Computer Art. MIT Press, Cambridge
Ristani E, Solera F, Zou R, Cucchiara R, Tomasi C (2016) Performance measures and a data set for multi-target, multi-camera tracking. European Conference on Computer Vision workshop on Benchmarking Multi-Target Tracking
Saticky J (2019) A Duke study recorded thousands of students’ faces. Now they’re being used all over the world. The Chronicle. https://dukechronical.com. Accessed June 2019
Seaver N (2017) Algorithms as culture: Some tactics for the ethnography of algorithmic systems. Big Data Society 4(2):205395171773810
Solera F, Calderara S, Ristani E, Tomasi C, Cucchiara R (2016) Tracking social groups within and across cameras. IEEE Trans Circ Syst Video Technol 27(3):441–453
Sound Vision Foundation (2019) About Uighurs. Save Uighur Campaign. https://doi.org/saveuighur.org. Accessed Oct 2019
Steyerl H (2014) Proxy Politics: Signal and Noise. e-flux journal. https://www.e-flux.com/journal/60/61045/proxy-politics-signal-and-noise/. Accessed May 2019
Wang J (2017) Carceral Capitalism. MIT Press, Cambridge
Xu J, Zhao R, Zhu F, Wang H, Ouyang W (2018) Attention-aware compositional network for person re-identification. 2018 IEEE/CVF conference on computer vision and pattern recognition, pp 2119–2128
Acknowledgements
Thanks to Brett Zehner, Quran Karriem, Benjamin Crais, Jordan Sjol, Sophia Goodfriend, Mark Hansen, Mark Olson, Luciana Parisi, and the extended community around Computational Media, Arts & Cultures at Duke University for their input and comments.
Funding
Not applicable.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Uliasz, R. Seeing like an algorithm: operative images and emergent subjects. AI & Soc 36, 1233–1241 (2021). https://doi.org/10.1007/s00146-020-01067-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00146-020-01067-y