Algorithmic Harms in Child Welfare: Uncertainties in Practice, Organization, and Street-level Decision-making
Bibliographic record
Abstract
Algorithms in public services such as child welfare, criminal justice, and education are increasingly being used to make high-stakes decisions about human lives. Drawing upon findings from a two-year ethnography conducted at a child welfare agency, we highlight how algorithmic systems are embedded within a complex decision-making ecosystem at critical points of the child welfare process. Caseworkers interact with algorithms in their daily lives where they must collect information about families and feed it to algorithms to make critical decisions. We show how the interplay between systemic mechanics and algorithmic decision-making can adversely impact the fairness of the decision-making process itself. We show how functionality issues in algorithmic systems can lead to process-oriented harms where they adversely affect the nature of professional practice, and administration at the agency, and lead to inconsistent and unreliable decisions at the street level. In addition, caseworkers are compelled to undertake additional labor in the form of repair work to restore disrupted administrative processes and decision-making, all while facing organizational pressures and time and resource constraints. Finally, we share the case study of a simple algorithmic tool that centers caseworkers’ decision-making within a trauma-informed framework and leads to better outcomes, however, required a significant amount of investments on the agency’s part in creating the ecosystem for its proper use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".