Addressing the Challenges of Government Service Provision with Artificial Intelligence
Bibliographic record
Abstract
In complete contract theory, the main approach to limit moral hazard is through modifying incentives for the agents. However, such modifications are not always feasible. One prominent example is Chinese government service provision. Over the years, it has been plagued with inefficiencies as a result of moral hazard. Previous attempts to address these challenges are not effective, as reforms on civil servant incentives face stiff hindrance. In this article, we report an alternative platform — SmartHS — to address these challenges in China without modifying incentives. Through dynamic teamwork, automation of key steps involved in service provision, and improved transparency with the help of artificial intelligence, it places civil servants into an environment that promotes efficiency and reduces the opportunities for moral hazard. Deployment tests in the field of social insurance service provision in three Chinese cities involving close to 3 million social insurance service cases per year demonstrated that the proposed approach significantly reduces moral hazard symptoms. The findings are useful for informing current policy discussions on government reform in China and have the potential to address long‐standing problems in government service provision to benefit almost one‐fifth of the world's population.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".