Building an AI Ethics and Governance Framework for Enterprise Applications

# Example AI Ethics Principles Document
ai_ethics_principles:
  fairness:
    definition: "AI systems must treat all individuals and groups equitably"
    key_requirements:
      - "Identify and mitigate bias in training data"
      - "Test for disparate impact across protected groups"
      - "Ensure equal access and quality of service"
    
  transparency:
    definition: "AI systems must be explainable and understandable"
    key_requirements:
      - "Document model development process and decisions"
      - "Provide appropriate explanations for AI decisions"
      - "Disclose when AI is being used in decision-making"
    
  privacy:
    definition: "AI systems must respect user privacy and data rights"
    key_requirements:
      - "Minimize collection and use of personal data"
      - "Implement privacy-preserving techniques"
      - "Provide mechanisms for user control over data"

# Example AI risk assessment framework
def assess_ai_system_risk(system_metadata):
    """
    Assess the risk level of an AI system based on various factors
    
    Args:
        system_metadata: Dictionary containing system information
        
    Returns:
        risk_score: Overall risk score (1-5)
        risk_factors: Dictionary of individual risk factors
    """
    risk_factors = {}
    
    # Assess data sensitivity
    data_sensitivity = {
        "public": 1,
        "internal": 2,
        "confidential": 3,
        "restricted": 4,
        "regulated": 5
    }
    risk_factors["data_sensitivity"] = data_sensitivity.get(system_metadata.get("data_classification", "internal"), 2)
    
    # Assess decision impact
    decision_impact = {
        "informational": 1,
        "operational": 2,
        "significant": 3,
        "major": 4,
        "critical": 5
    }
    risk_factors["decision_impact"] = decision_impact.get(system_metadata.get("decision_impact", "operational"), 2)
    
    # Calculate overall risk score (weighted average)
    weights = {
        "data_sensitivity": 0.4,
        "decision_impact": 0.6
    }
    
    risk_score = sum(risk_factors[factor] * weights[factor] for factor in risk_factors)
    
    return risk_score, risk_factors

# AI Ethics Policy

## Purpose
This policy establishes guidelines for the ethical development and use of artificial intelligence (AI) systems within [Company Name]. It applies to all employees, contractors, and partners involved in the development, deployment, or use of AI systems.

## Scope
This policy applies to all AI systems developed, deployed, or used by [Company Name], including machine learning models, natural language processing systems, computer vision applications, and automated decision-making systems.

## Principles

### 1. Fairness and Non-discrimination
- AI systems must be designed to treat all individuals and groups fairly
- Training data must be assessed for bias and mitigated appropriately
- AI systems must be tested for disparate impact across protected groups
- Fairness metrics must be defined and monitored for each AI system

### 2. Transparency and Explainability
- The purpose and capabilities of AI systems must be clearly communicated
- High-risk AI systems must provide appropriate explanations for their decisions
- Documentation must be maintained for all aspects of the AI lifecycle
- Users must be informed when interacting with or being subject to AI systems

### 3. Privacy and Data Governance
- AI systems must respect user privacy and comply with data protection regulations
- Data minimization principles must be applied to AI training and operation
- Privacy-enhancing technologies should be implemented where appropriate
- Clear data retention and deletion policies must be established

# AI Risk Classification Framework
risk_levels:
  - level: 1
    name: "Minimal Risk"
    description: "AI systems with minimal potential for harm"
    examples:
      - "Internal productivity tools"
      - "Data visualization systems"
    requirements:
      - "Standard development practices"
      - "Basic documentation"
      
  - level: 2
    name: "Low Risk"
    description: "AI systems with limited potential for harm"
    examples:
      - "Content recommendation systems"
      - "Customer segmentation"
    requirements:
      - "Data quality assessment"
      - "Basic fairness testing"
      - "Model documentation"

# AI Ethics Impact Assessment

## System Information
- **System Name**: [Name]
- **Description**: [Brief description]
- **Business Purpose**: [Business purpose]
- **Risk Classification**: [Risk level]
- **Data Sources**: [List of data sources]
- **Affected Stakeholders**: [List of stakeholders]

## Fairness Assessment
- **Protected Attributes**: [List attributes]
- **Fairness Metrics Used**: [List metrics]
- **Fairness Testing Results**: [Summary of results]
- **Mitigation Measures**: [Describe measures]

## Transparency Assessment
- **Model Type**: [Model type]
- **Explainability Method**: [Method used]
- **Documentation Status**: [Status]
- **User Communication Plan**: [Plan summary]

## Privacy Assessment
- **Personal Data Used**: [Types of data]
- **Data Minimization Measures**: [Measures taken]
- **Privacy-Enhancing Technologies**: [Technologies used]
- **Data Retention Policy**: [Policy summary]

# Example fairness testing framework
import numpy as np
import pandas as pd
from sklearn.metrics import confusion_matrix

def calculate_fairness_metrics(y_true, y_pred, sensitive_features):
    """
    Calculate fairness metrics across sensitive feature groups
    
    Args:
        y_true: True labels
        y_pred: Predicted labels
        sensitive_features: DataFrame of sensitive attributes
        
    Returns:
        metrics: Dictionary of fairness metrics by group
    """
    metrics = {}
    
    # Overall metrics
    overall_cm = confusion_matrix(y_true, y_pred)
    overall_tn, overall_fp, overall_fn, overall_tp = overall_cm.ravel()
    
    metrics["overall"] = {
        "accuracy": (overall_tp + overall_tn) / (overall_tp + overall_tn + overall_fp + overall_fn),
        "precision": overall_tp / (overall_tp + overall_fp) if (overall_tp + overall_fp) > 0 else 0,
        "recall": overall_tp / (overall_tp + overall_fn) if (overall_tp + overall_fn) > 0 else 0,
        "false_positive_rate": overall_fp / (overall_fp + overall_tn) if (overall_fp + overall_tn) > 0 else 0,
        "false_negative_rate": overall_fn / (overall_fn + overall_tp) if (overall_fn + overall_tp) > 0 else 0
    }
    
    # Calculate metrics for each sensitive feature
    for column in sensitive_features.columns:
        metrics[column] = {}
        
        # Calculate metrics for each group within the feature
        for group in sensitive_features[column].unique():
            group_mask = (sensitive_features[column] == group)
            group_y_true = y_true[group_mask]
            group_y_pred = y_pred[group_mask]
            
            # Skip if group is too small
            if len(group_y_true) < 10:
                continue
                
            # Calculate confusion matrix
            cm = confusion_matrix(group_y_true, group_y_pred)
            tn, fp, fn, tp = cm.ravel()
            
            # Calculate metrics
            metrics[column][group] = {
                "count": len(group_y_true),
                "accuracy": (tp + tn) / (tp + tn + fp + fn) if (tp + tn + fp + fn) > 0 else 0,
                "precision": tp / (tp + fp) if (tp + fp) > 0 else 0,
                "recall": tp / (tp + fn) if (tp + fn) > 0 else 0,
                "false_positive_rate": fp / (fp + tn) if (fp + tn) > 0 else 0,
                "false_negative_rate": fn / (fn + tp) if (fn + tp) > 0 else 0
            }
    
    return metrics

# Model Documentation

## Model Overview
- **Model Name**: [Name]
- **Version**: [Version number]
- **Date Created**: [Date]
- **Last Updated**: [Date]
- **Model Type**: [Type]
- **Purpose**: [Purpose]
- **Intended Use Cases**: [Use cases]
- **Out-of-Scope Use Cases**: [Out-of-scope uses]

## Data
- **Training Data Sources**: [Sources]
- **Data Timeframe**: [Timeframe]
- **Data Preprocessing**: [Preprocessing steps]
- **Feature Engineering**: [Feature engineering details]
- **Data Splits**: [Training/validation/test splits]
- **Data Biases and Limitations**: [Known biases and limitations]

## Model Details
- **Algorithm**: [Algorithm details]
- **Hyperparameters**: [Key hyperparameters]
- **Model Architecture**: [Architecture details]
- **Training Procedure**: [Training procedure]
- **Evaluation Metrics**: [Metrics used]
- **Performance Results**: [Performance summary]
- **Fairness Evaluation**: [Fairness metrics]