Who Has an Interest in “Public Interest Technology”?: Critical Questions for Working with Local Governments & Impacted Communities
Bibliographic record
Abstract
Local governments use a wide array of software, algorithms, and data systems across domains such as policing, probation, child protective services, courts, education, public employment services, homelessness services, etc. A growing body of work in CSCW and HCI has emerged to study, design, or demonstrate the boundaries of these technologies, oftentimes working with local governments. Local governments ostensibly aim to serve the public. So, some prior work has collaborated with local governments in the name of the public interest. However, others argue that local governments primarily police poor, minoritized communities, especially with increasingly limited funding for public services such as education or housing. These tensions raise critical questions: (How) should researchers collaborate with local governments? When should we oppose governments? How do we ethically engage with communities without being extractive? In this one-day workshop, we will bring together researchers from academia, the public sector, and community organizations to first take stock of work around public interest technologies. We will reflect on critical questions to orient the future of public interest technology and how we can work with, around, or against local governments while centering impacted communities.
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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 | 0.125 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.053 | 0.101 |
| Scholarly communication | 0.044 | 0.065 |
| Open science | 0.007 | 0.034 |
| Research integrity | 0.026 | 0.034 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".