Service demand
Forecast applications, cases, inspections, or calls so public teams can plan capacity without restricting access.
MACHINE LEARNING USE CASES
Machine learning for public services & risk
Policy analysis · risk triage · fairness · resource allocation
Public-service datasets often cover eligibility, income, safety, demand, or risk. They explain the mechanics of classification. They also show why prediction and decision-making aren't the same thing. Historical outcomes reflect institutions, access, measurement choices, and past policy. They don't reflect individual behavior alone.
A classification model estimates whether a recorded outcome falls into one of several groups. That estimate may help analysts study patterns or prioritize voluntary support. High-impact uses require legal authority, strong validation, transparency, appeal routes, and human accountability. A predictable pattern isn't always appropriate or useful to predict.
REAL DATA, REAL RESULTS
Public services & risk datasets and benchmarks
Each row connects a business problem to its dataset profile, held-out benchmark results, and prepared Google Sheet. Dataset size provides context. It isn't a quality score. Compare results only when the tasks and metrics match.
| Dataset | Problem | Task | Rows | Features | Evidence | Carla |
|---|---|---|---|---|---|---|
| Adult | Income classification | Binary classification | 48,842 | 14 | Benchmark | Try Adult in Google Sheets |
| Compas Two Years | Recidivism and fairness research | Binary classification | 5,278 | 13 | Benchmark | Try Compas Two Years in Google Sheets |
| Titanic | Historical survival analysis | Binary classification | 1,309 | 11 | Benchmark | Try Titanic in Google Sheets |
FROM QUESTION TO PREDICTION
What can machine learning do in public services & risk?
A worthwhile use case has an outcome you can define, examples of past outcomes, inputs available at prediction time, and a workflow that can act on the result.
Support prioritization
Identify cases that may benefit from faster contact or additional assistance, with safeguards against exclusion.
Policy analysis
Explore how recorded attributes and outcomes relate, while distinguishing descriptive association from causal impact.
Risk triage
Prioritize qualified review without letting an error determine a person's rights, liberty, or access to service.
Fairness auditing
Compare error rates and model behavior across relevant groups and investigate the data-generating process behind disparities.
INPUTS AND TARGETS
What kind of data is useful?
Start with fields that exist before the prediction is made. Remove identifiers unless they serve a defensible purpose, and watch for columns that reveal the outcome indirectly.
- Application or case attributes available at the actual decision time
- Service interactions, wait times, outcomes, and follow-up periods
- Geographic or organizational context at an appropriate aggregation level
- Documented policy changes and timestamps needed to interpret historical shifts
READ THE EVIDENCE
What do the benchmarks tell you?
Carla’s benchmark pages use persisted held-out splits. Models train on one set of rows and run on another. This keeps comparisons reproducible across the browser and Python runners. It doesn't remove the differences between a public dataset and your process.
Use the benchmark to inspect task formulation, metrics, variability, and runtime. Then repeat the evaluation on your own historical data with a split that resembles future use.
- The Adult and COMPAS datasets are widely used in fairness research. Apparent predictive performance can coexist with consequential subgroup disparities.
- Random splits answer a narrow replication question. Public systems that change over time may require temporal, geographic, or organizational holdouts before any operational claim.
- Historical labels may measure institutional action rather than the underlying concept. Recorded enforcement, for example, isn't identical to unobserved behavior.
RESPONSIBLE USE
Privacy and decision safeguards
Public and demographic data can expose sensitive attributes or enable consequential profiling. Collect and use only what is necessary, document access, and assess risks to individuals and groups before modeling.
Do not use these demonstration datasets as ready-made decision systems. High-impact public uses need domain expertise, community and stakeholder input, impact assessment, contestability, ongoing monitoring, and a defensible legal basis.
Before deployment, document the intended user, decision, target definition, acceptable errors, excluded uses, and a process for monitoring changes. Keep a manual path for unusual cases and for people affected by consequential decisions.
START WITH YOUR OWN TABLE
Try Carla Have your own public services & risk dataset?
Run a model for rows and columns directly on your data in Google Sheets. Carla runs it locally in your browser. Your spreadsheet doesn't need to go to a model server.
PRACTICAL QUESTIONS
Frequently asked questions
What is a good first machine learning project in Public Services & Risk?
Choose a repeated decision with a measurable historical outcome, data available before the decision, and a person who can act on the prediction. Begin with a narrow classification or regression question rather than trying to automate an entire process.
Do I need to be a data scientist to test these examples?
No. The linked dataset pages explain the target and benchmark, and Carla lets you run a tabular foundation model from Google Sheets. Production decisions still require appropriate domain, data, and governance expertise.
Does benchmark performance guarantee results on my data?
No. A benchmark measures a model on a specific dataset and evaluation design. Your population, definitions, data quality, time period, and workflow can produce materially different results.