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Accepted Contribution:

Navigating and drawing through a data-set: An effort to circumvent the meaning boxes way of seeing   
Gabriella Gonçalles (Hochschule für Kunst Bremen)

Short abstract:

This panel will present an artistic approach to visualize the relationships between images within the invisible part of a dataset - a part that has not been identified by the Google API. How can art point to narratives and landscapes that have been erased - and to those that have yet to be created?

Long abstract:

Denise Scott Brown and Robert Venturi, influential architects in the field of urban planning, stated in "Learning from Las Vegas" that "learning from the existing landscape is, for the architect, a way of being revolutionary". Such learning is possible through the use of numerous tools: drawing, photography, data collection - but perhaps one of these tools is the most effective of all, which is, as both architects suggest - the gaze.

This panel will therefore present an artistic project that aims to visualize potential relationships between portions given as invisible images - the result of an algorithmic censorship. The project used images shared on Google Maps - a map and image visualization service - as a dataset to 1) discuss how machines are changing the nature of vision (Azar et al) and, therefore, our knowledge of the constructed-scape and 2) understand, through a disobedient stance, which landscapes we are failing to see and stories we are failing to tell. We are perhaps facing the most paradoxical situation of potentially creating the richest and most plural visual culture in history through access to the media and "being plunged into the limbo of the uniformity of the gaze" (Beiguelman). However, training sets are increasingly part of our cities infrastructure and therefore have the "power to shape the world in their own images" (Crawford & Paglen). Which potential landscapes (and narratives) are we failing to be agents of in this process?

Combined Format Open Panel P116
Experiments with computer vision: transforming and re-envisioning visual data
  Session 1 Friday 19 July, 2024, -