The Open Prison of the Big Data Revolution: False Consciousness, Faustian Bargains, and Digital Entrapment
Bibliographic record
Abstract
Although some scholars raise alarm about societal harm emerging from Big Data practices, critical social theory (CST) Information Systems research on the structures and dynamics driving Big Data practices is rare. In this research commentary, we interrogate how tech firms use social practices and platform design to strategically manipulate individuals into accepting datafication and data assetization that accrue positive data network effects for themselves and mostly negative data network effects (economic loss, social and privacy harm) for individuals. We draw on the ideas of Heidegger and Marcuse to critically question the Big Data paradigm in order to develop better understanding of the social implications for individuals and society. Using the concepts of false consciousness, digital entrapment, and Faustian bargains, we critically inquire into the Big Data practices that keep us tethered to digital platforms. Specifically, we interrogate sociomaterial structures that socially condition individuals into a digital habitus and to identify themselves as homo digitalis, who view all their “relations” (social and economic) as digital. This social conditioning reproduces a false consciousness that constricts our worldview, undermines our rational choices, and enables the risky compromises we make with tech companies that manipulate and exploit us with their increasingly oppressive Big Data practices and related dark patterns. We critically analyze the case of Microsoft Viva to provide an illustration of how mundane digital tools can condition our reality and entrap us into an open prison. We argue that if we do not critically interrogate our false consciousness of the digital and understand how digital giants colonize our social systems by structurally embedding Big Data practices, we will continue to be susceptible to manipulation and digital entrapment. Ongoing risky compromises with tech firms will erode the very foundations of the “good life,” freedom, liberty, and personal privacy, and they will institutionalize the open prison. The CST explanation we propose and the research agenda we outline are meant to encourage research into solutions to the digital entrapment problem. History: Suprateek Sarker, Senior Editor; Robert Gregory, Associate Editor. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2020.0588 .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.128 |
| Scholarly communication | 0.020 | 0.029 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".