“Problems” with AI: How Artificial Intelligence Helps Frame and Formulate Problems in Organization
Bibliographic record
Abstract
This symposium investigates the intersection of two topics that are gaining traction in academic conversations across the Academy. The first is based on a phenomenon, which is the rapid rise of generative AI technologies that are transforming work and posing serious questions about how they will affect human productivity and well-being. The other is based on theory, which is the rising awareness that effective strategies for innovation and management depend on their fit with the nature of problems that they aim to solve, which in turn depends on how well individuals frame and formulate problems. Scholars have been theorizing on the importance of framing and formulating “problems” for several decades, but the topics have received growing interest in recent years due to prominent publications highlighting their fundamental role in (a) defining opportunities to pursue during strategic decision-making, (b) providing a focal point for gaining resources in entrepreneurial ventures, and (c) creating the fundamental context in which problem-solving occurs to drive creativity and innovation. Problem framing and formulation are likely to become even more important for organizational actors in organizational research because generative AI technologies are effective in and increasingly capable of creating solutions across various tasks, modalities, and contexts once problems are clearly defined. Therefore, this symposium is both timely and relevant to help shape the conversation on these important topics, which will be facilitated by many leading voices in the field. Human-AI Interaction: Dividing the Labor, Interrogating, Doom and Delight Author: Hila Lifshitz-Assaf; Warwick Business School Author: Steven Randazzo; Warwick Business School Envision the Future or Critique the Past: A Theory of Evaluating Problems for Innovation Author: Johnathan Cromwell; U. of San Francisco Author: Jean-François Harvey; HEC Montréal Author: Andy El-Zayaty; Leavey School of Business, Santa Clara U. Machine Predictions and Causal Explanations: Evidence from a Field Experiment Author: Xi Kang; Vanderbilt U. Author: Hyunjin Kim; INSEAD Understanding Motivations of Open Artificial Intelligence Developers: Evidence from GitHub Author: Savindu Herath; ETH Zurich Author: Patrick Tinguely; ETH Zürich
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.057 |
| Scholarly communication | 0.028 | 0.037 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".