Catch me if you can: Using machine learning and behavioral interventions to reduce unethical behavior
Bibliographic record
Abstract
Abstract We report the results of a field experiment designed to increase honest disclosure of claims at a U.S. state unemployment agency. Individuals filing claims were randomized to a message (‘nudge’) intervention, while an off-the-shelf machine learning algorithm calculated claimants’ risk for committing fraud (underreporting earnings). We study the causal effects of algorithmic targeting on the effectiveness of nudge messages: Without algorithmic targeting, the average treatment effect of the messages was insignificant; in contrast, the use of algorithmic targeting revealed significant heterogeneous treatment effects across claimants. Claimants predicted to behave unethically by the algorithm were more likely to disclose earnings when receiving a message relative to a control condition, with claimants predicted to most likely behave unethically being almost twice as likely to disclose earnings when shown a message. In addition to providing a potential blueprint for targeting more costly interventions, our study offers a novel perspective for the use and efficiency of data science in the public sector without violating citizens’ agency. However, we caution that, while algorithms can enable tailored policy, their ethical use must be ensured at all times.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".