Our research shows that machine vision is more than a technology. It is actively imagined, contextually situated, and historically complex. Machine vision is not only about seeing – it does things, and it is biased at every level. Despite the prevalence of dystopic stories where machine vision is used to oppress humans, we also see that machine vision is constantly negotiated, opening up a potential for change.
Key outputs include:
- The curation of a publicly available dataset with structured interpretations of how humans and other entities interact with machine vision technologies in 500 movies, novels, digital artworks and video games. Software for analysing the dataset has been published, and there is a data paper describing the dataset and how it can be used by other researchers.
- The development of an analysis model, "machine vision situations", that allows both close and distant readings of how agency is represented or functions when humans and technologies interact. The model was developed for analysing our dataset, but will also be useful for other studies of technology, both in media studies and literary studies and in ethnographic analyses of real-world interactions with technology. A feature of this model is that it enables both large scale analysis and close readings.
- A series of analyses of specific digital artworks, films, television series and video games with a focus on how machine vision is represented and used in cultural modes of discourse.
- Cross-cultural anthropological analyses of machine vision in a Chinese context, in Europe and in the USA.
- Development of concepts such as "cyborg vision", which helps understand the double way of seeing the world through both our human eyes and technical systems, and which we are trained in through video games and other forms of entertainment.
- An understanding of how digital artworks function as artistic audits that test and critique existing AI systems and how this can impact technological development and public discourse
- A method for using "algorithmic failure" or machine learning's failure to predict the correct answer to identify rich cases for qualitative analysis in a large dataset.
- A monograph titled Machine Vision: How Algorithms are Changing the Way We See the World (Polity Press 2023) and many public lectures on the topic
- We used creative methods in addition to critical theory and analysis. Team member Linda Kronman developed the artwork Suspicious Behaviour (2020) with collaborator Andreas Zingerle, allowing users to experience how a clickworker is required to code datasets for AI-based surveillance. The work was shown at several exhibitions. Kronman and Zingerle won the 2022 Austrian Outstanding Artist award in the media arts category. (
https://kairus.org/suspicious/(odnośnik otworzy się w nowym oknie))
- We developed a series of larps (live action roleplaying games) exploring visual surveillance in 2020-2021, and in 2024 a larp exploring social and cultural impacts on new visual and algorithmic technologies in the 1840s centred around Charles Babbage's Saturday night soirées.
- Three PhD students were trained in the project, Ragnhild Solberg, Linda Kronman and Marianne Gunderson, with the last defending her dissertation in Nov 2024. Each now has extensive expertise in critical theories and technical knowledge related to generative AI and AI-based image recognition, and specialised knowledge in their field: game studies for Solberg, digital art for Kronman and narratives and social media for Gunderson.
The research is summarised in a visually presented report that can be downloaded at
https://hdl.handle.net/11250/3158329(odnośnik otworzy się w nowym oknie)
