Indic classifier fairness and drift monitor
An evaluation dashboard for measuring accuracy, calibration, subgroup performance, and model drift in classifiers that process selected Indic-language text.
Project definition
Problem statement
A classifier can have acceptable overall accuracy while performing poorly for a particular language, script, code-mixing pattern, text length, or underrepresented class. Performance can also change when real input differs from the reference dataset.
The engineering problem is to build an evaluation system that makes these differences visible, tests whether gaps are supported by enough data, detects distribution change, and records the limitations of every result.
Project objectives
- Register versioned datasets, classifier outputs, labels, and documented evaluation slices.
- Calculate overall, class-level, language-level, and intersectional performance metrics.
- Measure probability calibration and compare decision thresholds.
- Detect feature, prediction, confidence, and performance drift using prepared scenarios.
- Generate reproducible reports with sample counts, uncertainty, and model limitations.
System design
System modules
Dataset registry
Stores dataset versions, label definitions, permitted slice fields, sample counts, splits, and source documentation.
Model registry
Records model versions, intended task, class labels, thresholds, training reference, and prediction files or endpoints.
Metric engine
Calculates overall and sliced classification metrics with minimum sample rules and confidence intervals.
Calibration analyser
Measures confidence reliability and compares error and review tradeoffs at selected thresholds.
Drift monitor
Compares reference and current windows for input features, predictions, confidence, and available labelled performance.
Investigation dashboard
Shows alerts, affected slices, trends, sample counts, metric uncertainty, and linked model and dataset versions.
Methodology
System workflow
- 01Register reference
A prepared labelled dataset and model version are recorded with their task, classes, languages, and approved slice definitions.
- 02Run evaluation
Predictions are joined to ground truth and metrics are calculated overall and for slices that meet minimum sample rules.
- 03Set thresholds
The user compares confidence, error, and review rates and saves a documented operating threshold.
- 04Load monitoring window
A prepared current dataset is compared with the reference distribution and model-output history.
- 05Investigate alert
The dashboard links a drift or performance alert to affected classes, language slices, sample counts, and example errors.
Demonstration scenario
A multilingual classifier and its reference test set are registered. The dashboard reveals similar overall accuracy but lower recall for one language and poorer calibration for transliterated text. A simulated current-data window changes the script distribution, triggers a drift alert, and links the alert to the affected metrics and examples.
Engineering
Technical architecture
- Web application
- Nuxt and Vue for registry records, metric comparison, calibration plots, drift timelines, alerts, and report export.
- Evaluation API
- FastAPI for dataset ingestion, prediction validation, metric jobs, monitoring windows, alerts, and reports.
- Data layer
- PostgreSQL for datasets, models, predictions, labels, slices, metrics, thresholds, monitoring windows, and alert history.
- Analysis engine
- Python, Pandas, and scikit-learn for classification metrics, bootstrap intervals, calibration, distribution tests, and drift measures.
- Monitoring rules
- Versioned minimum-sample, comparison-window, threshold, severity, and acknowledgement rules for each registered model.
- Delivery
- Docker-based setup with prepared multilingual classification data, simulated drift windows, and reproducible evaluation commands.
Testing
Evaluation
Evaluation measures
- Correctness of overall and sliced metric calculations against prepared cases
- Macro F1, per-class recall, false-positive rates, and calibration error by language slice
- Confidence-interval coverage and behaviour at small sample sizes
- Detection rate and false-alert rate across prepared drift scenarios
- Traceability from each result to dataset, model, threshold, and configuration versions
- Evaluation runtime and dashboard response time at prepared data volumes
System boundaries
- The monitor measures documented statistical differences and does not certify that a model is fair or safe.
- Slices use permitted evaluation attributes and avoid exposing personal information.
- Metrics with insufficient samples are suppressed or clearly marked as unreliable.
- A qualified reviewer decides whether an alert requires data collection, model changes, threshold changes, or no action.
Included
- 01Dataset, model, evaluation, and monitoring dashboard
- 02Performance-slice, calibration, and drift-analysis pipelines
- 03Threshold comparison and alert investigation tools
- 04Prepared datasets, drift scenarios, and evaluation results
- 05Complete source code in a private GitHub repository
- 06Complete project documentation with synopsis, abstract, methodology, system diagrams, experiment results, screenshots, and conclusion
- 07Setup and usage guide
Project record
No buyer information is collected on this page.
- Permanent project ID
- GP-DA-0DMXAEX
- Catalogued
- 21 Aug 2026
- Completed
- Pending
- Verified
- Pending
- Demonstration
- Added when ready
Handover
After purchase
- 01Payment is confirmed
The project is marked unavailable and cannot be purchased again.
- 02Repository access is granted
The buyer's submitted GitHub account receives access to the private repository.
- 03The purchase record is delivered
The certification sheet is prepared from the reviewed buyer details and sent privately by email.