Beyond Data Collection: Examining Artificial Intelligence Data Creation in Organizations
Bibliographic record
Abstract
Current management literature conceptualizes “data collection” — or the organizational process of gathering inputs to train and validate transformational technologies — as a bounded process involving technical work, limited interaction between stakeholders, and finite time. In our 16-month ethnographic study of a healthcare organization developing artificial intelligence (AI) systems, the project team initially approached data collection for AI as literature predicts. We found, however, the project team engaging in a process of data creation involving expansive interactions across different occupations, spanning many organizational practices, and involving diverse stakeholders. Our findings uncovered four consequential, but overlooked, components of the expansive data creation process: what is the phenomenon for which an AI model should be built, what is considered data about the phenomenon, which data can be collected, and which data are ultimately recorded. As a result, this paper’s central insight is that rather than conceptualizing data for AI in organizations as a raw, independent, objective resource which is collected through a bounded process, our study highlights how data is contextual, subjective, and dependent, and is actively created through an expansive, iterative approach within organizations. We discuss the theoretical and empirical implications of our results for organizations pursuit of transformational technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".