Black Box Precision: Unlocking High-Stakes Performance with Explainable AI
npm install blackboxpcsUnlocking High-Stakes Performance with Explainable AI



The Black Box Precision SDK resolves the dilemma between AI performance and interpretability. It enables you to harness maximum AI power while simultaneously integrating Explainable Artificial Intelligence (XAI) techniques to ensure transparency, safety, and accountability—without sacrificing performance.
This SDK is specifically designed for high-stakes environments where errors carry catastrophic consequences (e.g., medical diagnostics, autonomous systems, military applications, financial systems).
- 🔬 SHAP Integration: Theoretical gold standard for feature attribution
- ⚡ LIME Integration: Fast, intuitive local explanations
- 🌐 Global & Local Explanations: Support for both auditing and operational oversight
- 🛡️ High-Stakes Ready: Built for mission-critical applications
- 📊 Comprehensive Utilities: Tools for validation, aggregation, and audit trails
The package is available on npm:
``bash`
npm install blackboxpcs
📦 npm package: https://www.npmjs.com/package/blackboxpcs
Install dependencies:
`bash`
pip install -r requirements.txt
Or install as a package:
`bash`
pip install -e .
`python
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from blackboxpcs import BlackBoxPrecision, ExplanationType, ExplanationMode
print("Prediction:", result["predictions"])
print("SHAP Explanation:", result["explanations"]["shap"])
print("LIME Explanation:", result["explanations"]["lime"])
`
`python
from blackboxpcs import BlackBoxPrecision, ExplanationType
bbp = BlackBoxPrecision(
model=diagnosis_model,
explainer_type=ExplanationType.SHAP,
feature_names=["lesion_density", "lesion_size", "patient_age", ...],
class_names=["benign", "malignant"]
)
print(f"Diagnosis: {result['predictions']}")
print(f"Key factors: {top_features['features']}")
`
`python
from blackboxpcs import BlackBoxPrecision, ExplanationType
bbp = BlackBoxPrecision(
model=perception_model,
explainer_type=ExplanationType.LIME,
feature_names=[f"pixel_{i}" for i in range(2242243)] # Image features
)
print(f"Decision: {result['predictions']}")
print(f"Key factors: {top_features['top_features']}")
`
`pythonPerform comprehensive model audit
audit_results = bbp.audit_model(
X_train,
y=y_train,
explanation_type=ExplanationType.SHAP
)
print("Model Accuracy:", audit_results.get("accuracy"))
print("Feature Importance:", audit_results["explanations"]["shap"]["feature_importance_ranking"])
`
- SHAP (SHapley Additive exPlanations): Provides mathematical guarantees for feature attribution. Ideal for post-mortem auditing and regulatory compliance.
- LIME (Local Interpretable Model-agnostic Explanations): Fast, intuitive explanations perfect for real-time operational oversight.
- Local (Operational): Generate explanations for individual predictions in real-time
- Global (Auditing): Analyze model behavior across datasets to detect biases and validate system behavior
Main framework class for integrating XAI with black box models.
`python`
BlackBoxPrecision(
model: Any,
explainer_type: ExplanationType = ExplanationType.BOTH,
feature_names: Optional[List[str]] = None,
class_names: Optional[List[str]] = None,
**kwargs
)
Key Methods:
- explain(X, mode, explanation_type): Generate explanationsexplain_local(X)
- : Generate local explanations for operational useexplain_global(X)
- : Generate global explanations for auditingpredict_with_explanation(X)
- : Make predictions with immediate explanationsaudit_model(X, y)
- : Perform comprehensive model auditing
`python`
SHAPExplainer(
model: Any,
feature_names: Optional[List[str]] = None,
class_names: Optional[List[str]] = None,
background_data: Optional[np.ndarray] = None,
algorithm: str = "auto",
**kwargs
)
`python`
LIMEExplainer(
model: Any,
feature_names: Optional[List[str]] = None,
class_names: Optional[List[str]] = None,
mode: str = "classification",
num_features: int = 10,
**kwargs
)
The SDK includes utility functions for common tasks:
- validate_explanation(): Validate explanation completenessaggregate_explanations()
- : Aggregate multiple explanationsformat_explanation_for_audit()
- : Format explanations for audit trailscompare_explanations()
- : Compare two explanationsextract_key_features()
- : Extract top contributing features
Black Box Precision embraces the full complexity of deep AI, viewing the "Black Box" as a source of unparalleled power, not a failure of design. Our approach is built on three non-negotiable pillars:
1. Depth of Insight: Utilize complex models to their full capacity
2. Trust through Results: Generate verifiable explanations for every decision
3. Application in Critical Fields: Designed for high-stakes environments
Contributions are welcome! Please see our contributing guidelines for details.
MIT License - see LICENSE file for details
If you use Black Box Precision in your research, please cite:
```
Black Box Precision: Unlocking High-Stakes Performance with Explainable AI
The XAI Lab, 2025
For issues, questions, or contributions, please open an issue on GitHub.
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The time to choose is now: Demand Black Box Precision.