Bibliographic record
Abstract
Abstract This chapter evaluates the incompatible incentives of private-sector AI. Private-sector investment in AI is dominated by major internet platform companies such as Facebook, Amazon, Apple, Google, and Microsoft. These platform companies are also leaders in deploying deep learning algorithms. Although deep learning algorithms may be more intelligent than previous generations of machine learning, they are not more robust. There may be a faint technical path forward for problems of bias and unfairness, but algorithms are engines, and pervasive incompatible incentives will remain. As such, algorithms require guardrails. However, technology companies are ill-suited and ill-positioned to design or implement these value-based rules. Guardrails become constraints on people’s behavior, and yet, in cases of high elasticity, effective governance may still be elusive. Ultimately, the pairing of the algorithm and guardrails tempts companies to engage in regulatory arbitrage, providing a requirement for external action.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".