📋 Tender Overview
ExtractiveSeeing
Grant agreement ID: 101266960
DOI
10.3030/101266960
EC signature date
24 August 2026
Start date
1 January 2027
End date
31 December 2031
Funded under
European Research Council (ERC)
Total cost
No data
EU contribution
€ 2 481 349,00
Objective
ExtractiveSeeing researches the new paradigm of photography embedded in contemporary mobile phone cameras and promoted as AI-assisted since 2024. With the widespread daily use of mobile phone cameras, AI is rapidly transforming visual experience, being used to extract elements from existing images to compose new images or remove unwanted objects from scenes, This appears to reproduce the model of seeing of the AI models themselves, which are pre-trained to identify objects in image datasets, treating elements of an image as disposable and interchangeable. The process resembles the ways that material extraction treats nature and entities (including people) as resources to be used or disposed of, and existing research shows that current AI and digital photography are dependent on extractive practices for mineral resources as well as vast amounts of water and energy. ExtractiveSeeing is the first research project on AI-assisted mobile phone cameras that examines the extractive image processing involved in AI image making, asking what ways of seeing the world are being normalised via the integration of generative AI into mobile phone cameras. It examines both the technologies and their promotion in relation to resource extraction and trained ways of seeing. To locate precedents and understand the novelty of the ways of photographing that mobile phone companies are promoting, ExtractiveSeeing uses archival research into past methods and norms of amateur photography, and draws on philosophical aesthetics, ecology and art theory. Overall, it will characterize the modes of seeing being built into everyday photography, comprehending these in relation to historical practices and current environmental concerns. ExtractiveSeeing will develop an innovative, critical approach that extends beyond AI-assisted photography, to address the connections between the technical and material ways they construct images, and the ways of seeing they normalise.
