Organizing Work with Algorithmic Augmentation and Artificial Intelligence
Bibliographic record
Abstract
With the rise of artificial intelligence (AI), we see renewed interest in algorithms that underlie all digital work. The aim of this symposium is to gather our different understandings of algorithms across different disciplines and divisions, and make sense of what we know and what is important to focus on next. Current scholarship shows us that algorithms - a set of digital instructions that are implemented to achieve a goal - enable humans to work more effectively and augment our capacities. At the same time, algorithms are affordances that, in use, can manifest consequences differently to plans and design. Algorithms organize, manage, and control many tasks that are delegated to them by humans; however, this control is not always obvious, transparent, or equitably balanced. We also know that machine learning algorithms are moving beyond initial design to offer unique solutions for unsupervised and uncertain environments and problem spaces. There is growing anticipation about the possibilities of AI and algorithms at work. We hope to unpack current scholarship in this symposium and seek overlap between different domains interested in algorithms and AI in organizational settings and beyond. AI in Organizations: Research Opportunities Presenter: Robert Channing Seamans; NYU Stern The Fine Lines of Dissent of Working with Algorithms Presenter: Emmanuelle Vaast; McGill U. Doubting the Diagnosis: The Role of Ambiguity When Forming Professional Judgments with AI Tools Presenter: Natalia Levina; New York U. Presenter: Sarah Lebovitz; U. of Virginia Presenter: Hila Lifshitz-Assaf; New York U. Organizing in the Age of Organic Machines Presenter: Youngjin Yoo; Case Western Reserve U.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".