Artificial intelligence and organizational memory in government: the experience of record duplication in the child welfare sector in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent years, the topic of artificial intelligence in government has become a major area of study. Governments have been eager to adopt artificial intelligence for a number of purposes, including for the prediction of risk in social services. Child protection services are exploring predictive analytics for the initial screening of cases. While research identifies data quality issues as a major barrier, little is known about the characteristics of these issues in child protection, their relationship to organizational memory contained in administrative data, and their impact on the ability of an organization to adopt these technologies. This study gained insight into the socio-technical limitations of duplicate records when trying to bring organizational memory to bear in predictive decision support by interviewing and observing staff use of information technology systems. The study's findings suggest that record duplication in case management systems in child protection could pose a significant challenge to the introduction of artificial intelligence technologies such as predictive analytics for decision assistance. There is a need to address foundational information management and system issues before artificial intelligence approaches such as this can be introduced in the child protection sector.
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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 | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it