Project Maven, Big Data, and Ubiquitous Knowledge: The Impossible Promises and Hidden Politics of Algorithmic Security Vision

Hogue, Simon

doi:10.1007/978-3-030-73276-9_10

Cited by 4 publications

(2 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The automatic identification of anomalies out of similar data may avoid, as Aradau and Blanke (2018) suggest, identifying norms beforehand. Yet the whole process of creating the databases from which algorithms work involves selecting, filtering, indexing, and excluding to keep only the types of data deemed relevant to identifying risks (Hogue 2021). In other words, even if anomaly detection does not ask programmers to set queries beforehand, developers still need to select specific types of data thought to provide insight-communication metadata to reconstruct social graphs or geolocation data to build mobility patternsand then leave analysts to judge the security value of the flagged data afterward.…”

Section: The Efficient Machine: Normalizing Algorithmic Surveillance ...mentioning

confidence: 99%

Person of Interest as Media Technology of Surveillance: A Cautionary Tale for the Future of the National Security State With Diegetic Big Data Surveillance, Algorithmic Security, and Artificial Intelligence

Grondin,

Hogue

2023

Television & New Media

Self Cite

View full text Add to dashboard Cite

In this article, we examine the television series about global surveillance Person of Interest ( POI) (aired on CBS, 2011–2016) to reflect on popular understandings of surveillance and account for its representation of artificial intelligence (AI) and algorithmic surveillance. Drawing on television, media, and surveillance studies, we focus on the power of representations of social relations and diegetic technologies in possible and imagined futures to explore the role of cultural representations in shaping social order. Through the character of the AI Machine representing algorithmic surveillance, we evaluate the show’s critique of algorithmic autonomy and contend that, as a media technology of surveillance, the show participates in the hype of big data as a panacea while banalizing surveillance. We argue that POI could facilitate a comprehensive analysis of the politics of algorithmic surveillance but fails to do so due to its uncritical representation of artificial intelligence agencies in detecting security risks.

show abstract

Section: The Efficient Machine: Normalizing Algorithmic Surveillance ...mentioning

confidence: 99%

Person of Interest as Media Technology of Surveillance: A Cautionary Tale for the Future of the National Security State With Diegetic Big Data Surveillance, Algorithmic Security, and Artificial Intelligence

Grondin,

Hogue

2023

Television & New Media

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, Project Maven has not yet undergone rigorous scientific testing. Investigating the usefulness of a Maven-like recognition aid for enhancing sensemaking is important because other projects that the U.S. Defense Department is planning on undertaking build upon the design of Project Maven (Hogue, 2021).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Simulated Recognition Aids for Human Sensemaking in Applied Surveillance Scenarios

et al. 2022

View full text Add to dashboard Cite

Objective We determined whether the human capability for sensemaking, or for identifying essential elements of information (EEIs), could be enhanced by a simulated recognition aid that directed attention to people and vehicles in scenes or by a simulated recognition aid that directed attention to EEIs. Background For intelligence analysts, sensemaking is challenging because it frequently involves making inferences about uncertain data. One way to enhance sensemaking may involve collaboration from a machine recognition aid such as Project Maven, an established algorithm that directs analysts’ attention to people and vehicles in scenes. We simulated the directed attention of Project Maven as well as a machine recognition aid that directed attention to EEIs. Method We created full-motion videos of simulated compounds viewed by an overhead camera. Sensemaking was assessed by measuring participants’ ability to predict events and identify signs. Participants’ attention was directed by placing small globe symbols above either all people and vehicles, or all EEIs. Novices and intelligence analysts participated. Results Simulated recognition aids directing participants’ attention to EEIs improved EEI identification but directing attention to people and vehicles (emulating Project Maven) did not. Participants’ sensemaking was not enhanced by either type of simulated recognition aid. Conclusion Guiding attention to features in a scene improves their identification whereas indiscriminate steering of attention to entities in the scene does not improve understanding of the holistic meaning of events, unless attention is drawn to relevant signs of those events. Application Results contribute to our goal of determining which human-machine systems improve the sensemaking capability of intelligence analysts in the field.

show abstract

The metadata-driven killing apparatus: big data analytics, the target selection process, and the threat to international humanitarian law

Badalič

2023

Critical Military Studies

View full text Add to dashboard Cite

Project Maven, Big Data, and Ubiquitous Knowledge: The Impossible Promises and Hidden Politics of Algorithmic Security Vision

Cited by 4 publications

References 28 publications

Person of Interest as Media Technology of Surveillance: A Cautionary Tale for the Future of the National Security State With Diegetic Big Data Surveillance, Algorithmic Security, and Artificial Intelligence

Person of Interest as Media Technology of Surveillance: A Cautionary Tale for the Future of the National Security State With Diegetic Big Data Surveillance, Algorithmic Security, and Artificial Intelligence

Evaluation of Simulated Recognition Aids for Human Sensemaking in Applied Surveillance Scenarios

The metadata-driven killing apparatus: big data analytics, the target selection process, and the threat to international humanitarian law

Contact Info

Product

Resources

About