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Paper short abstract

Computed images combine maps and visual imagery but differ from photographs: elements may be absent or generated without visible cues. The paper argues that claims of precision in the context of AI-driven technologies depend on questioning habits of seeing and critically assessing plausibility.

Paper long abstract

Precision has become a central promise in contemporary technological approaches to the efficient and responsible distribution of resources. An important aspect of such systems is the human evaluation of displayed data in constellations, where automated analysis and human judgment are deliberately combined. Increasingly, drones equipped with sensors and AI-based processing are deployed to generate situational images intended to support decision-making.

The interpretation of these images involves a distinctive visual configuration. They combine familiar representational formats—most notably maps and photographic imagery—with a comparatively new form of technical output: computed images. Rather than direct recordings, these images are produced through algorithmic processing and inference, translating heterogeneous sensor data into visual outputs that appear immediately readable.

Computed images differ in a crucial way from camera-based photographs. There, absences or anomalies often produce visual irritations that invite closer scrutiny. In computed imagery, by contrast, information may simply not appear without any visible indication that something is missing—for example when areas are left blank or when the system lacks data. Conversely, computed images may display objects or patterns that are not present in that form in the observed environment but emerge from the system’s calculations and models.

The paper argues that the emphasis on precision cannot be understood solely as a property of technical systems. It must be coupled with the human capacity to critically assess plausibility and to question established habits of technological seeing. Otherwise, the promise of precision risks obscuring errors and undermining the reliability such systems claim to provide.