AI and Big Tech in Regulated Industries: Navigating Risks, Innovation, and Public Value
Bibliographic record
Abstract
This panel symposium will explore the transformative impact of Artificial Intelligence (AI) and Big Tech platforms in regulated industries such as healthcare and finance. By reshaping traditional business models, regulatory frameworks, and societal expectations, technology companies influence the evolution of these sectors in unprecedented ways. Drawing on insights from three continents—North America, Europe, and Australia—panelists will explore the opportunities and risks associated with Big Tech’s growing dominance. Topics will include its phased approach to entering regulated markets, the emergence of new governance actors, and the risks of misaligned incentives that could undermine public value. The symposium will examine sector-specific challenges posed by AI, including data privacy concerns, market dominance, and ethical dilemmas in algorithmic decision-making. The panel aims to address cross-sectoral understanding of responsible AI governance from diverse disciplines— technology management, strategy, computer science, public policy, organization studies, and bioethics. By prioritizing public value and strategic competitiveness, this dialogue will inform future regulatory strategies to ensure AI’s alignment with societal interests.
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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 | 0.027 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.025 | 0.018 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.007 | 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".