Black Box Precision Core SDK

Unlocking High-Stakes Performance with Explainable AI


Overview

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).

Key Features

- 🔬 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

Installation

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The package is available on npm:

``bash
npm install blackboxpcs
`

📦 npm package: https://www.npmjs.com/package/blackboxpcs

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Install dependencies:

`bash
pip install -r requirements.txt
`

Or install as a package:

`bash
pip install -e .
`

Quick Start

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`python
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from blackboxpcs import BlackBoxPrecision, ExplanationType, ExplanationMode

Train a black box model

print("Prediction:", result["predictions"])
print("SHAP Explanation:", result["explanations"]["shap"])
print("LIME Explanation:", result["explanations"]["lime"])
`

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`python
from blackboxpcs import BlackBoxPrecision, ExplanationType

Medical diagnosis model

print(f"Diagnosis: {result['predictions']}")
print(f"Key factors: {top_features['features']}")
`

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`python
from blackboxpcs import BlackBoxPrecision, ExplanationType

Autonomous vehicle perception model

print(f"Decision: {result['predictions']}")
print(f"Key factors: {top_features['top_features']}")
`

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`python


Perform comprehensive model audit

print("Model Accuracy:", audit_results.get("accuracy"))
print("Feature Importance:", audit_results["explanations"]["shap"]["feature_importance_ranking"])
`

Core Concepts

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- 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.

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- Local (Operational): Generate explanations for individual predictions in real-time
- Global (Auditing): Analyze model behavior across datasets to detect biases and validate system behavior

API Reference

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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 explanations
- explain_local(X): Generate local explanations for operational use
- explain_global(X): Generate global explanations for auditing
- predict_with_explanation(X): Make predictions with immediate explanations
- audit_model(X, y): Perform comprehensive model auditing

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`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
)
`

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`python
LIMEExplainer(
model: Any,
feature_names: Optional[List[str]] = None,
class_names: Optional[List[str]] = None,
mode: str = "classification",
num_features: int = 10,
**kwargs
)
`

Utilities

The SDK includes utility functions for common tasks:

- validate_explanation(): Validate explanation completeness
- aggregate_explanations(): Aggregate multiple explanations
- format_explanation_for_audit(): Format explanations for audit trails
- compare_explanations(): Compare two explanations
- extract_key_features(): Extract top contributing features

Use Cases

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`
Black Box Precision: Unlocking High-Stakes Performance with Explainable AI
The XAI Lab, 2025
``

Support

For issues, questions, or contributions, please open an issue on GitHub.

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