Paradoxes of AI in Organizations
Bibliographic record
Abstract
Divergent and often contradictory findings suggest that the study of AI in organizations is rife with paradox, persistent contradictions between interdependent elements (Schad, Lewis, Raisch, & Smith, 2016); And yet, management research has thus far fallen short in capturing the complexity of AI’s organizational and societal implications (Raisch & Krakowski, 2021). Given the sheer preponderance of contradictory perspectives in the management literature on AI, such a meta-theoretical approach offers substantive opportunity for capturing the full richness of the dynamic interactions between AI and organizational contexts. For this symposium session, we hope to shine a spotlight on the tensions evoked by AI in organizations and encourage openness to complexity in the rapidly growing literature on AI’s role in organizational systems. Specifically, our presentations will discuss the interdependencies and tensions involved in navigating over-reliance and under-reliance on AI, control and empowerment of employees via AI-powered systems, collaboration and competition with AI as a counterpart, scientific and commercial demands in AI development, and learning and performance goals in implementing AI tools on an organizational level. Navigating Complexity and Tensions in AI Governance in Healthcare Author: Elisabeth Yang; Yale University When Doing the Right Thing is a Moving Target: How Ethical Concerns Evolve as AI Progresses Author: Emmanuelle Vaast; McGill University Algorithmic Frenemies: When Cooperating While Competing with Generative AI Facilitates Learning Author: Benjamin Stephen Poag; New York University Author: Batia Mishan Wiesenfeld; New York University How Does Information Transparency Influence Employees on a Digital Work Platform? Author: Yan Zhang; Beyond Technology: How Organizations Shape Human-AI Collaboration Author: Nan Jia; University of Southern California Author: Albert Choi Roh; University of Southern California
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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 | 0.029 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.009 | 0.064 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".