Crowd-Sourced Intelligence Agency: Prototyping counterveillance. Issue 1 (February 2017)
- Record Type:
- Journal Article
- Title:
- Crowd-Sourced Intelligence Agency: Prototyping counterveillance. Issue 1 (February 2017)
- Main Title:
- Crowd-Sourced Intelligence Agency: Prototyping counterveillance
- Authors:
- Gradecki, Jennifer
Curry, Derek - Abstract:
- This paper discusses how an interactive artwork, the Crowd-Sourced Intelligence Agency (CSIA), can contribute to discussions of Big Data intelligence analytics. The CSIA is a publicly accessible Open Source Intelligence (OSINT) system that was constructed using information gathered from technical manuals, research reports, academic papers, leaked documents, and Freedom of Information Act files. Using a visceral heuristic, the CSIA demonstrates how the statistical correlations made by automated classification systems are different from human judgment and can produce false-positives, as well as how the display of information through an interface can affect the judgment of an intelligence agent. The public has the right to ask questions about how a computer program determines if they are a threat to national security and to question the practicality of using statistical pattern recognition algorithms in place of human judgment. Currently, the public's lack of access to both Big Data and the actual datasets intelligence agencies use to train their classification algorithms keeps the possibility of performing effective sous-dataveillance out of reach. Without this data, the results returned by the CSIA will not be identical to those of intelligence agencies. Because we have replicated how OSINT is processed, however, our results will resemble the type of results and mistakes made by OSINT systems. The CSIA takes some initial steps toward contributing to an informed public debateThis paper discusses how an interactive artwork, the Crowd-Sourced Intelligence Agency (CSIA), can contribute to discussions of Big Data intelligence analytics. The CSIA is a publicly accessible Open Source Intelligence (OSINT) system that was constructed using information gathered from technical manuals, research reports, academic papers, leaked documents, and Freedom of Information Act files. Using a visceral heuristic, the CSIA demonstrates how the statistical correlations made by automated classification systems are different from human judgment and can produce false-positives, as well as how the display of information through an interface can affect the judgment of an intelligence agent. The public has the right to ask questions about how a computer program determines if they are a threat to national security and to question the practicality of using statistical pattern recognition algorithms in place of human judgment. Currently, the public's lack of access to both Big Data and the actual datasets intelligence agencies use to train their classification algorithms keeps the possibility of performing effective sous-dataveillance out of reach. Without this data, the results returned by the CSIA will not be identical to those of intelligence agencies. Because we have replicated how OSINT is processed, however, our results will resemble the type of results and mistakes made by OSINT systems. The CSIA takes some initial steps toward contributing to an informed public debate about large-scale monitoring of open source, social media data and provides a prototype for counterveillance and sousveillance tools for citizens. … (more)
- Is Part Of:
- Big data & society. Volume 4:Issue 1(2017)
- Journal:
- Big data & society
- Issue:
- Volume 4:Issue 1(2017)
- Issue Display:
- Volume 4, Issue 1 (2017)
- Year:
- 2017
- Volume:
- 4
- Issue:
- 1
- Issue Sort Value:
- 2017-0004-0001-0000
- Page Start:
- Page End:
- Publication Date:
- 2017-02
- Subjects:
- Dataveillance -- information access -- transparency -- social media -- counterveillance -- sousveillance
Big data -- Social aspects -- Periodicals
Social sciences -- Research -- Data processing -- Periodicals
Social sciences -- Research -- Methodology -- Periodicals
Data mining -- Periodicals
300.28557 - Journal URLs:
- http://bds.sagepub.com ↗
http://www.uk.sagepub.com/home.nav ↗ - DOI:
- 10.1177/2053951717693259 ↗
- Languages:
- English
- ISSNs:
- 2053-9517
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 7618.xml