The Impact of Digitalization on the Economy: A Review Article on the NBER Volume "Economics of Artificial Intelligence: An Agenda"
Bibliographic record
Abstract
Digitalization is affecting every aspect of our economy and our society. A set of new technologies are behind this latest surge - robotic process automation, artificial intelligence (AI) and machine learning (ML), big data, cloud computing, the internet of things and blockchain. This volume, "The Economics of Artificial Intelligence", focuses on the impact, real and prospective, of machine learning (ML), on the economy. The authors tackle a wide range of topics, including how it is impacting innovation, the consequences for employment and economic growth, issues related to privacy, international trade and ultimately, how AI will affect the economics discipline itself. The contributors, overall, take a positive view of the impact of AI on economic outcomes. They also acknowledge, however, that policies related to redistribution, privacy and competition are needed to ensure that the benefits of digitalization are shared appropriately.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".