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 falsepositives, 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.
Objective Delay of gratification, or the extent to which one can resist the temptation of an immediate reward and wait for a larger reward later, is a self-regulatory skill that predicts positive outcomes. The aim of this research was to conduct initial tests of the effects of a board game designed to increase children’s delay of gratification via two experimental studies. Methods Preschool children were randomized to play the study game or a control game. In Study 1, there were 48 children in the analytic sample, with a mean age of 4.81 ± 0.55 years; Study 2 included 50 children ( M = 4.02 ± 0.76 years). Delay of gratification was assessed during the study game, as well as before and after game play sessions using the Marshmallow Test. Results In both studies, the intervention group’s likelihood of delaying gratification during the study game increased across game-play sessions ( p < 0.05). In Study 1, the intervention group also increased wait times during the Marshmallow Test versus controls ( p = 0.047). In Study 2, there was no effect on Marshmallow Test wait times. Conclusion Results provide some initial evidence supporting potential efficacy of a board game designed to increase delay of gratification. Future research can clarify: (1) which components of game play (if any) are linked with broader changes in delay of gratification, (2) impacts of this intervention in more diverse samples, and (3) whether experimental manipulation of delay of gratification affects outcomes like achievement and weight, which have been linked to this skill in observational studies.
Predictive algorithms are indeterminate when they are used on data that differs from their training set, which is always a possibility in real-world applications. Anthropomorphic metaphors can obfuscate the differences between human perception and the computational processes that comprise algorithmic decision-making. This article shows how artistic uses of predictive algorithms can reveal the algorithms’ indeterminate nature and raise questions about the efficacy and dangers of algorithmic decision-making. In particular, the technique of artistic defamiliarization is presented as a way question the foundations of positivist epistemologies that equate quantification and calculation with objective truth.
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