Spaces:

Hugdrigo
/

modelguard-observatory

Running

App Files Files Community

modelguard-observatory / METHODS.md

Hugdrigo

Upload 28 files

85920d3 verified 30 days ago

preview code

Raw History Blame Contribute Delete

3.01 kB

# ModelGuard Observatory · Methods

## Purpose
This tool supports structured auditing of *evaluation snapshots* for a small multilingual academic-intent classification benchmark. It is NOT a streaming production observability service, model safety certificate, or scientific external-validity study.

## Metrics
- Accuracy = correct predictions / labeled samples.
- Macro-F1 = average of per-class F1 for labels with support > 0, calculated using precision = TP / (TP + FP) and recall = TP / (TP + FN). Zero denominators contribute 0.
- Generalization gap = controlled IID accuracy minus novel-pattern challenge accuracy (reported in percentage points). The splits differ in design: it is not a time-series degradation rate.
- Error confusion: row = true label, column = predicted label; raw counts.

### Dataset distribution shift
- **Jensen–Shannon divergence** uses base-two logarithms and has range [0, 1] bits. It compares language distributions and true-label distributions across the two evaluation splits.
- **Population Stability Index** uses fixed bins `[0,5,10,15,20,30,50,100001]` on **the number of unique lowercase token types per sentence** (not the full word count). For baseline proportions `p_i` and challenge proportions `q_i`, PSI = `Σ_i (q_i - p_i) * ln(q_i/p_i)` with both proportions floored at `1e-4` before each term to avoid numerical singularities. This is a heuristic sensitive to binning and sample size.
- A difference in distributions indicates a diagnostic shift; it does not establish its cause or statistical significance.

### Paired comparison and uncertainty
Paired differences are computed from identically identified samples across two methods within the same challenge split. A seeded nonparametric bootstrap resamples paired examples 500 times and reports descriptive percentile bounds. This interval is **not** an external population coverage guarantee, nor is it a confirmatory significance test. The package also contains an accuracy bootstrap helper for reproducibility.

## Configurable release checklist
Default thresholds are deliberately explicit and adjustable. They are engineering heuristics, not peer-reviewed standards:

1. Challenge examples >= 50.
2. Challenge accuracy >= 75%.
3. Controlled-to-challenge gap <= 22 percentage points.
4. At least 10 challenge examples in each language slice.
5. Language-slice accuracy >= 58%.
6. Length histogram PSI <= 0.50.

The challenge length distribution shifts enough that **all four baseline methods fail the default PSI criterion**. The gate does not perform auto-retraining, auto-approval, or overrides. Lowering thresholds can yield a pass without improving the model; never interpret PASS as a safety or deployment certification.

## Bias and fairness limitations
Language and academic-domain groupings are not demographic-protected classes. Aggregate or subgroup comparisons in this synthetic task cannot establish demographic fairness, equitable outcomes, privacy compliance, or production suitability.

	# ModelGuard Observatory · Methods

	## Purpose
	This tool supports structured auditing of evaluation snapshots for a small multilingual academic-intent classification benchmark. It is NOT a streaming production observability service, model safety certificate, or scientific external-validity study.

	## Metrics
	- Accuracy = correct predictions / labeled samples.
	- Macro-F1 = average of per-class F1 for labels with support > 0, calculated using precision = TP / (TP + FP) and recall = TP / (TP + FN). Zero denominators contribute 0.
	- Generalization gap = controlled IID accuracy minus novel-pattern challenge accuracy (reported in percentage points). The splits differ in design: it is not a time-series degradation rate.
	- Error confusion: row = true label, column = predicted label; raw counts.

	### Dataset distribution shift
	- Jensen–Shannon divergence uses base-two logarithms and has range [0, 1] bits. It compares language distributions and true-label distributions across the two evaluation splits.
	- Population Stability Index uses fixed bins `[0,5,10,15,20,30,50,100001]` on the number of unique lowercase token types per sentence (not the full word count). For baseline proportions `p_i` and challenge proportions `q_i`, PSI = `Σ_i (q_i - p_i) * ln(q_i/p_i)` with both proportions floored at `1e-4` before each term to avoid numerical singularities. This is a heuristic sensitive to binning and sample size.
	- A difference in distributions indicates a diagnostic shift; it does not establish its cause or statistical significance.

	### Paired comparison and uncertainty
	Paired differences are computed from identically identified samples across two methods within the same challenge split. A seeded nonparametric bootstrap resamples paired examples 500 times and reports descriptive percentile bounds. This interval is not an external population coverage guarantee, nor is it a confirmatory significance test. The package also contains an accuracy bootstrap helper for reproducibility.

	## Configurable release checklist
	Default thresholds are deliberately explicit and adjustable. They are engineering heuristics, not peer-reviewed standards:

	1. Challenge examples >= 50.
	2. Challenge accuracy >= 75%.
	3. Controlled-to-challenge gap <= 22 percentage points.
	4. At least 10 challenge examples in each language slice.
	5. Language-slice accuracy >= 58%.
	6. Length histogram PSI <= 0.50.

	The challenge length distribution shifts enough that all four baseline methods fail the default PSI criterion. The gate does not perform auto-retraining, auto-approval, or overrides. Lowering thresholds can yield a pass without improving the model; never interpret PASS as a safety or deployment certification.

	## Bias and fairness limitations
	Language and academic-domain groupings are not demographic-protected classes. Aggregate or subgroup comparisons in this synthetic task cannot establish demographic fairness, equitable outcomes, privacy compliance, or production suitability.