Omnipresence: Machine Vision in the Adversarial City
Item
-
Title
-
Omnipresence: Machine Vision in the Adversarial City
-
Description
-
This work follows the trail of Omnipresence, a mobile program of public safety lighting in New York City. Unpacking its historical and design precedents, it argues that Omnipresence is just the latest component of an urban dispositive as old as the modern city itself: one that illustrates the connections between public lighting, organized policing, racial hierarchies, and bureaucracies for collecting spatial data about the city and its inhabitants. Part of a massive network of AI-driven surveillance technologies called the Domain Awareness System; this work fuses the digital tools of landscape architecture with machine learning models to reverse engineer Omnipresence, visualize how the Domain Awareness system sees and crafts the urban landscape, and propose design interventions that neutralize their harms and craft "Dark Commons." Finally, it strives to make all of the research, AI tools, and design interventions available to the public for use, development, and collaboration.
-
Creator
-
Booz, Justin Andrew
-
Subject
-
Artificial Intelligence
-
Blackness
-
Landscape Architecture
-
Machine Learning
-
Policing
-
Surveillance
-
Landscape architecture
-
Artificial intelligence
-
Design
-
Contributor
-
Douglas, Craig
-
Waldheim, Charles
-
Date
-
2024-05-21T12:06:31Z
-
2024
-
2024-05-16
-
2024
-
2024-05-21T12:06:31Z
-
Type
-
Thesis or Dissertation
-
text
-
Format
-
application/pdf
-
application/pdf
-
Identifier
-
Booz, Justin Andrew. 2024. Omnipresence: Machine Vision in the Adversarial City. Master's thesis, Harvard Graduate School of Design.
-
31298773
-
https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37378616
-
Language
-
en