"""
Stage information content for ML Builder pipeline stages.

Contains titles, descriptions, requirements, and ethical considerations
for each stage of the ML pipeline.
"""

from enum import Enum
from typing import Dict, Any

class ModelStage(Enum):
    DATA_LOADING = "data_loading"
    DATA_EXPLORATION = "data_exploration"
    DATA_PREPROCESSING = "data_preprocessing"
    FEATURE_SELECTION = "feature_selection"
    MODEL_SELECTION = "model_selection"
    MODEL_TRAINING = "model_training"
    MODEL_EVALUATION = "model_evaluation"
    MODEL_EXPLANATION = "model_explanation"
    SUMMARY = "summary"

STAGE_INFO = {
    ModelStage.DATA_LOADING: {
        "title": "Data Loading",
        "description": "Load your dataset and understand its basic properties.",
        "requirements": [
            "Dataset must be in CSV format",
            "Data must be in tabular format with rows as observations and columns as features",
            "Target variable must be numeric (for regression) or binary (for classification)",
            "Data should contain sufficient rows for analysis",
            "Column names should be present and unique"
        ],
        "ethical_considerations": [
            "Data collection methods and consent - ensure data was collected ethically",
            "Data privacy and security - check for sensitive or personally identifiable information",
            "Potential biases in data collection - consider if data is representative",
            "Data quality and completeness - assess missing values and their impact",
            "Data documentation - maintain clear records of data source and modifications"
        ]
    },
    ModelStage.DATA_EXPLORATION: {
        "title": "Data Exploration",
        "description": """
            Let's explore your data to understand what you're working with! This page offers:

            1. Duplicate Detection & Removal - Identifies and removes both exact duplicates
            2. Comprehensive Data Summary - Shows dataset overview, numerical/categorical analysis, data types, and missing values
            3. Interactive Analysis Sections:
               - Target Feature Analysis - Visualizes target distribution and feature-target relationships
               - Feature Analysis - Provides detailed visualisations for both numerical and categorical features
               - Feature Relationships - Analyses correlations and complex relationships between features
               - Data Quality Analysis - Assesses dataset health with completeness and validity metrics
            4. Auto Preprocessing - Optional one-click data preparation including train/test splitting

            Each section includes interactive visualisations and actionable insights to help you understand your data better.
        """,
        "requirements": [
            "**Duplicate Analysis**\n"
            "- Automatically detects exact duplicates (identical rows)\n"
            "- Identifies partial duplicates (same features, different targets)\n"
            "- Provides duplicate removal with detailed statistics\n"
            "- Shows percentage of data affected by duplicates",

            "**Data Overview Dashboard**\n"
            "- Presents key metrics (rows, columns, missing values)\n"
            "- Includes interactive data preview with filtering\n"
            "- Displays detailed data type analysis with visualisations\n"
            "- Shows comprehensive numerical and categorical summaries",

            "**Target Feature Analysis**\n"
            "- Visualizes target distribution with appropriate charts\n"
            "- Automatically detects classification vs. regression problems\n"
            "- Analyses feature-target relationships with statistical metrics\n"
            "- Provides strength assessments for predictive relationships",

            "**Interactive Feature Analysis**\n"
            "- Offers histogram/density plots for numerical features\n"
            "- Displays bar charts for categorical features\n"
            "- Shows outlier detection through box plots\n"
            "- Visualizes feature distributions segmented by target\n"
            "- Provides statistical significance tests",

            "**Feature Relationships Analysis**\n"
            "- Detects linear and non-linear relationships between features\n"
            "- Visualizes correlation matrices with interactive heatmaps\n"
            "- Identifies potential feature engineering opportunities\n"
            "- Helps detect multicollinearity issues",

            "**Data Quality Analysis**\n"
            "- Analyses missing value patterns and distributions\n"
            "- Provides missing value visualization and statistics\n"
            "- Assesses data completeness and consistency\n"
            "- Identifies potential data integrity issues",

            "**Auto Preprocessing Suite**\n"
            "- Performs intelligent train/test splitting\n"
            "- Handles missing values with advanced imputation\n"
            "- Processes categorical features with appropriate encoding\n"
            "- Handles outliers with winsorizing method\n"
            "- Removes highly correlated features\n"
            "- Provides detailed preprocessing dashboard"
        ],
        "ethical_considerations": [
            "**Bias Detection and Fairness**\n"
            "- Consider if data represents all demographic groups fairly\n"
            "- Watch for disproportionate missing values across groups\n"
            "- Be aware of how duplicate removal might affect representation\n"
            "- Consider how outlier handling impacts different groups\n"
            "- Check for target variable imbalance across protected attributes",

            "**Privacy Protection**\n"
            "- Identify and handle personally identifiable information\n"
            "- Review ID columns for potential privacy concerns\n"
            "- Consider data anonymization before sharing visualisations\n"
            "- Be cautious with detailed data previews\n"
            "- Apply proper data protection measures",

            "**Preprocessing Transparency**\n"
            "- Document all data transformations applied\n"
            "- Be transparent about auto-preprocessing decisions\n"
            "- Track feature removal and the reasoning behind it\n"
            "- Monitor how preprocessing affects different groups\n"
            "- Provide clear explanations for all automated processes",

            "**Data Quality Awareness**\n"
            "- Acknowledge data quality limitations\n"
            "- Be transparent about missing value handling approaches\n"
            "- Consider how imputation might introduce bias\n"
            "- Document any data quality issues discovered\n"
            "- Understand how quality issues might affect model outcomes",

            "**Target Variable Ethics**\n"
            "- Consider if the target variable contains implicit biases\n"
            "- Be aware of how target outlier handling affects predictions\n"
            "- Document any normative assumptions in the target\n"
            "- Watch for self-reinforcing patterns in target-feature relationships\n"
            "- Consider potential downstream impacts of target distribution"
        ]
    },
    ModelStage.DATA_PREPROCESSING: {
        "title": "Data Preprocessing",
        "description": "Prepare your data for machine learning by cleaning and transforming it into a format that models can understand. We'll guide you through handling missing data, converting categories to numbers, and ensuring your data is high quality.",
        "requirements": [
            "**Feature Management**\n"
            "- Review and understand your dataset structure\n"
            "- Remove unnecessary columns and duplicates\n"
            "- Ensure each column has the appropriate data type\n"
            "- Preview the impact of your changes",

            "**Zero Values**\n"
            "- Identify columns containing zeros\n"
            "- Determine if zeros represent missing data\n"
            "- Choose appropriate handling methods\n"
            "- Keep valid zeros, handle invalid ones",

            "**Train Test Split**\n"
            "- Split your data into training and testing sets\n"
            "- Uses an optimised ratio for the best results\n"
            "- Stops data leakage by seperating the data before preprocessing",

            "**Missing Values**\n"
            "- Visualize missing data patterns\n"
            "- Get smart suggestions for filling gaps\n"
            "- Choose from multiple strategies (mean, median, KNN)\n"
            "- Compare data before and after filling gaps",

            "**Variable Binning**\n"
            "- Group numerical values into meaningful ranges\n"
            "- Combine rare categories in categorical variables\n"
            "- Get automatic suggestions for optimal binning\n"
            "- Handle skewed distributions and outliers",

            "**Outliers**\n"
            "- Detect unusual values in your data\n"
            "- Understand the impact of outliers\n"
            "- Choose from multiple handling strategies\n"
            "- Preserve important extreme values",

            "**Feature Creation**\n"
            "- Create new features from existing ones\n"
            "- Use mathematical operations such as Mean, Sum, Product, Ratio and Difference\n"
            "- Create features that have strong predictive power while adding unique information to your dataset.",

            "**Categorical Encoding**\n"
            "- Convert text values to numbers\n"
            "- Get encoding suggestions per column\n"
            "- Choose from label, one-hot, or target encoding\n"
            "- Handle high-cardinality features",

            "**Data Type Optimisation**\n"
            "- Optimize memory usage\n"
            "- Convert columns to efficient types\n"
            "- Handle mixed-type columns\n"
            "- Ensure type consistency",

            "**Final Review**\n"
            "- Validate all preprocessing steps\n"
            "- Check data quality metrics\n"
            "- Review feature distributions\n"
            "- Confirm readiness for modeling"
        ],
        "ethical_considerations": [
            "**Data Quality**\n"
            "- Monitor the impact of transformations\n"
            "- Preserve important relationships\n"
            "- Maintain data integrity\n"
            "- Document all changes",

            "**Fairness**\n"
            "- Check for unintended bias\n"
            "- Preserve important group differences\n"
            "- Monitor impact on minority groups\n"
            "- Validate fairness metrics",

            "**Transparency**\n"
            "- Record all preprocessing decisions\n"
            "- Document the rationale for changes\n"
            "- Enable step-by-step review\n"
            "- Maintain clear audit trail",

            "**Privacy Protection**\n"
            "- Safeguard sensitive information\n"
            "- Prevent individual identification\n"
            "- Apply appropriate anonymization\n"
            "- Follow privacy guidelines",

            "**Reproducibility**\n"
            "- Save preprocessing configurations\n"
            "- Enable exact reproduction\n"
            "- Document transformation sequence\n"
            "- Maintain version control"
        ]
    },
    ModelStage.FEATURE_SELECTION: {
        "title": "🎯 Feature Selection",
        "description": """
            Let's choose the best features for your model! Feature selection helps you:

            1. 🎯 **Improve Model Performance** by removing irrelevant or redundant features
            2. 🚀 **Speed Up Training** by reducing dimensionality
            3. 📊 **Enhance Interpretability** by focusing on the most important features
            4. 🛡️ **Reduce Overfitting** by decreasing model complexity

            We'll guide you through this process with both automatic and manual options!
        """,
        "requirements": [
            "**Feature Analysis Results**\n"
            "- View feature importance scores and rankings\n"
            "- See correlations between features\n"
            "- Identify protected attributes\n"
            "- Review data quality issues",

            "**Feature Selection Methods**\n"
            "- Use Boruta algorithm for automated selection\n"
            "- Choose features manually from importance scores\n"
            "- Remove highly correlated feature pairs\n"
            "- Address features with quality issues",

            "**Duplicate Row Detection**\n"
            "- Find exact duplicate rows\n"
            "- Detect rows with same features but different targets\n"
            "- Review impact of duplicate removal",

            "**Dataset Preview**\n"
            "- View your selected features\n"
            "- See feature distributions\n"
            "- Analyse feature-target relationships\n"
            "- Review correlation patterns",

            "**Selection Management**\n"
            "- Track changes to your feature set\n"
            "- Compare the training and test datasets\n"
            "- Undo feature removal if needed\n"
            "- Monitor impact on data quality"
        ],
        "ethical_considerations": [
            "**Fairness in Selection**\n"
            "- Watch out for bias in feature removal\n"
            "- Check if changes affect groups differently\n"
            "- Keep track of sensitive information\n"
            "- Make sure your choices are fair to everyone",

            "**Data Quality**\n"
            "- Check data quality across different groups\n"
            "- Make sure feature removal is justified\n"
            "- Keep track of how changes affect your data\n"
            "- Document quality improvements",

            "**Privacy Protection**\n"
            "- Handle personal information carefully\n"
            "- Check for indirect identifying information\n"
            "- Consider privacy in your selections\n"
            "- Protect sensitive data",

            "**Documentation**\n"
            "- Record why you removed features\n"
            "- Track all your changes\n"
            "- Keep notes on important decisions\n"
            "- Make your process clear to others",

            "**Impact Analysis**\n"
            "- Check how changes affect model performance\n"
            "- Monitor impact on different groups\n"
            "- Verify data relationships are preserved\n"
            "- Ensure changes align with your goals"
        ]
    },
    ModelStage.MODEL_SELECTION: {
        "title": "Model Selection",
        "description": """
            In this step, we'll help you choose the right machine learning model for your data.
            We'll analyse your preprocessed data and problem type to recommend the best model
            for your specific needs.
        """,
        "requirements": [
            "**Understanding Your Problem**\n"
            "- We'll automatically check if your task is classification (predicting categories) or regression (predicting numbers)\n"
            "- Review the target variable characteristics\n"
            "- Confirm the detected problem type matches your goals\n"
            "- Understand what you're trying to predict",

            "**Analyzing Your Data**\n"
            "- Check the size of your dataset\n"
            "- Review the number of features\n"
            "- Detect any non-linear relationships\n"
            "- Assess data complexity",

            "**Model Options**\n"
            "- Review available models for your problem type\n"
            "- Understand each model's strengths\n"
            "- Learn about model limitations\n"
            "- Consider model requirements",

            "**Getting Recommendations**\n"
            "- See our suggested model based on your data\n"
            "- Understand the reasoning behind recommendations\n"
            "- Review how model options fit your needs\n"
            "- Consider computational requirements",

            "**Making Your Decision**\n"
            "- Select from recommended models\n"
            "- Review model characteristics\n"
            "- Consider your resource constraints\n"
            "- Confirm your selection"
        ],
        "ethical_considerations": [
            "**Model Transparency**\n"
            "- Consider if you need to understand how the model makes decisions\n"
            "- Balance between model complexity and interpretability\n"
            "- Think about whether stakeholders need to understand the model's logic\n"
            "- Consider regulatory requirements for model transparency",

            "**Fairness Potential**\n"
            "- Consider if the model type has known biases\n"
            "- Think about the model's ability to handle diverse data\n"
            "- Consider if the model can be evaluated for fairness\n"
            "- Review if the model type is appropriate for sensitive applications",

            "**Computational Impact**\n"
            "- Consider the environmental impact of model training\n"
            "- Think about the resources needed for this model type\n"
            "- Balance model complexity with resource usage\n"
            "- Consider if a simpler model could suffice",

            "**Known Limitations**\n"
            "- Review documented weaknesses of each model type\n"
            "- Consider potential failure modes in your context\n"
            "- Think about model-specific edge cases\n"
            "- Understand inherent model assumptions"
        ]
    },
    ModelStage.MODEL_TRAINING: {
        "title": "Model Training",
        "description": """
            Master the art of model training with comprehensive optimisation and analysis! 🚀

            This advanced training stage provides:
            1. **Dataset Overview**: Complete training/test split analysis with feature insights
            2. **Class Balance Management**: Imbalance detection and smart resampling
            3. **Intelligent Hyperparameter Optimization**: Choose between Random Search or advanced Optuna with Bayesian optimization
            4. **Educational Guidance**: Deep-dive explanations of cross-validation, parameter tuning, and optimization strategies
            5. **Model-Specific Tuning**: Adaptive parameter ranges for each algorithm (Decision Trees, Random Forest, XGBoost, Neural Networks, etc.)
            6. **Comprehensive Results Analysis**: Stability assessment, calibration analysis, and threshold optimization
            7. **Smart State Management**: Robust error handling with clear guidance for troubleshooting

            Every component includes interactive visualizations and educational content!
        """,
        "requirements": [
            {
                "title": "📊 Dataset Analysis & Overview",
                "items": [
                    "Comprehensive training/test dataset statistics and feature analysis",
                    "Training set size, feature count, and data distribution insights",
                    "Test set proportion analysis and validation split information",
                    "Feature name listing and data type verification",
                    "Memory usage and computational requirement assessments"
                ]
            },
            {
                "title": "⚖️ Advanced Class Balance Management",
                "items": [
                    "Automated class distribution analysis with interactive bar chart visualizations",
                    "Imbalance ratio calculation with severity classification (Mild/Moderate/Severe)",
                    "Smart resampling recommendations based on dataset characteristics and feature types",
                    "Multiple resampling techniques (SMOTE, ADASYN, Random Over/Under Sampling)",
                    "Before/after distribution comparison with visual impact assessment",
                    "Dataset characteristics analysis (numerical vs categorical features)"
                ]
            },
            {
                "title": "🎯 Intelligent Hyperparameter Optimization",
                "items": [
                    "Dual optimization methods: Random Search vs advanced Optuna with Bayesian optimization",
                    "Adaptive parameter ranges based on dataset characteristics (size, dimensionality, sparsity)",
                    "Smart cross-validation configuration (2-10 folds) with educational guidance",
                    "Flexible trial count selection (10-100) with performance trade-off analysis",
                    "Model-specific parameter explanations for all supported algorithms"
                ]
            },
            {
                "title": "📈 Comprehensive Results Analysis",
                "items": [
                    "Detailed training results with stability analysis and confidence interval calculations",
                    "Cross-validation metrics visualization with distribution analysis and outlier detection (Random Search)",
                    "Model selection comparison between 'Mean Score' and 'Stability-Performance Balance' criteria (Random Search)",
                    "Optuna optimization progress tracking with parameter evolution and trial pruning insights",
                    "Parameter importance analysis with interactive visualizations for all model types (Optuna)",
                    "Neural network architecture visualization with layer structure and neuron mapping (Optuna)",
                ]
            },
            {
                "title": "🎯 Model Calibration & Probability Analysis",
                "items": [
                    "Current model calibration analysis with reliability plots and Perfect Calibration reference",
                    "Brier Score and Expected Calibration Error (ECE) calculation for both binary and multiclass",
                    "Smart calibration method recommendations (Isotonic Regression vs Platt Scaling)",
                    "Interactive calibration visualization with perfect calibration diagonal reference",
                    "Calibration quality assessment with automatic recommendations for improvement",
                    "Calibration application with cross-validation and revert functionality"
                ]
            },
            {
                "title": "⚖️ Classification Threshold Optimization",
                "items": [
                    "Binary classification threshold optimization (automatic multiclass detection and warning)",
                    "Current performance analysis at default 0.5 threshold with confusion matrix visualization",
                    "Multiple optimization criteria (F1 Score, Precision, Recall, Accuracy, Youden's J)",
                    "Smart criterion recommendation based on data characteristics and class balance",
                    "ROC and Precision-Recall curves with optimal threshold visualization",
                    "Interactive threshold performance comparison with detailed metrics tables",
                    "Comprehensive educational content explaining threshold concepts and business implications"
                ]
            }
        ],
        "ethical_considerations": [
            {
                "title": "🔍 Model Reliability & Stability",
                "items": [
                    "Comprehensive stability assessment across multiple data splits and cross-validation folds",
                    "Performance consistency evaluation with confidence interval analysis",
                    "Overfitting detection and prevention through adaptive parameter ranges",
                    "Robust model selection criteria balancing performance with generalizability",
                    "Clear documentation of model limitations and reliability scores"
                ]
            },
            {
                "title": "⚖️ Fairness, Bias & Representation",
                "items": [
                    "Thorough class imbalance analysis with bias impact assessment",
                    "Ethical resampling strategy selection preserving data integrity",
                    "Performance equity evaluation across different data subgroups",
                    "Transparent documentation of all bias mitigation steps and their effects",
                    "Consideration of societal impact in optimization metric selection"
                ]
            },
            {
                "title": "🎯 Transparency & Interpretability",
                "items": [
                    "Complete hyperparameter search transparency with detailed parameter explanations",
                    "Clear documentation of optimization choices and their implications",
                    "Educational content ensuring user understanding of model behavior",
                    "Comprehensive logging of all training decisions and rationale",
                    "Accessible explanations of complex optimization concepts"
                ]
            },
            {
                "title": "🔐 Data Integrity & Security",
                "items": [
                    "Preservation of original data relationships during resampling",
                    "Secure handling of training data throughout the optimization process",
                    "Validation of data consistency before and after transformations",
                    "Protection against data leakage in cross-validation procedures",
                    "Comprehensive audit trail of all data modifications"
                ]
            }
        ]
    },
    ModelStage.MODEL_EVALUATION: {
        "title": "Model Evaluation",
        "description": """
            Comprehensive evaluation of your trained model's performance! 🎯

            This page provides a complete analysis of your model through several sections:

            📊 **Dataset Overview** - Compare training vs test data characteristics and class distributions

            📋 **Model Training Information** - Review your selected model details, optimization method (Optuna/Random Search), and hyperparameters

            **Four Main Evaluation Sections:**
            1. 📊 **Performance Metrics** - Detailed accuracy scores and statistical measures with explanations
            2. 📈 **Visualisations** - Interactive charts and plots showing model behavior and performance patterns
            3. 📋 **Sample Predictions** - Test your model on real examples with color-coded accuracy indicators
            4. 🚀 **Model Health and Recommendations** - Smart model insights and suggestions for model improvement

            **Additional Tools:**
            - 🔍 **Testing Data Exploration** - Interactive analysis of your test dataset
            - ⚙️ **Feature Transformation Details** - View how categorical encoding and feature binning were applied

            Each section includes comprehensive explanations to help you understand and interpret your model's performance.
        """,
        "requirements": [
            {
                "title": "📊 Dataset and Model Overview",
                "items": [
                    "Compare training and test dataset characteristics (samples, features, distributions)",
                    "Review selected model type and optimization method details",
                    "Examine complete hyperparameter configurations (tuned vs default)",
                    "View cross-validation metrics and selection criteria",
                    "Access interactive test data exploration tools"
                ]
            },
            {
                "title": "📈 Comprehensive Performance Metrics",
                "items": [
                    "For Classification: Accuracy, precision, recall, F1-score, ROC-AUC with detailed explanations",
                    "For Regression: R², MAE, MSE, RMSE, MAPE with interpretation guides",
                    "Cross-validation performance statistics and stability measures",
                    "Per-class performance breakdowns for multi-class problems",
                    "Performance confidence intervals and statistical significance"
                ]
            },
            {
                "title": "📊 Advanced Performance Visualisations",
                "items": [
                    "For Classification: Confusion matrices, ROC/PR curves, decision boundaries, feature importance",
                    "For Regression: Prediction scatter plots, residual analysis, error distributions, feature impact",
                    "Learning curves showing training vs validation performance over time",
                    "Interactive plots with detailed tooltips and explanations",
                    "Comparison visualisations for different performance aspects"
                ]
            },
            {
                "title": "🔍 Interactive Prediction Analysis",
                "items": [
                    "Review predictionsfrom your test dataset",
                    "Color-coded accuracy indicators for quick assessment",
                    "Prediction confidence scores and probability distributions (for classification)",
                    "Side-by-side comparison of predicted vs actual values"
                ]
            },
            {
                "title": "🚀 Model Insights",
                "items": [
                    "Automated model health assessment and performance diagnosis",
                    "Personalized recommendations for model improvement",
                    "Data quality insights and potential issues identification",
                    "Feature engineering suggestions based on performance patterns",
                    "Next steps guidance for model optimization and deployment"
                ]
            }
        ],
        "ethical_considerations": [
            {
                "title": "🔍 Performance Transparency and Accountability",
                "items": [
                    "Complete documentation of all evaluation metrics and methodologies",
                    "Clear explanations of metric calculations and interpretations",
                    "Transparent reporting of model limitations and potential biases",
                    "Accessible logging of all evaluation steps and decisions"
                ]
            },
            {
                "title": "⚖️ Fairness and Bias Assessment",
                "items": [
                    "Performance evaluation across different data segments and classes",
                    "Identification of systematic prediction errors or biases",
                    "Assessment of model fairness across protected characteristics",
                    "Guidance on interpreting performance differences responsibly"
                ]
            },
            {
                "title": "📊 Statistical Rigor and Reliability",
                "items": [
                    "Proper use of statistical measures and confidence intervals",
                    "Cross-validation results to assess model stability and generalizability",
                    "Clear distinction between training and test performance",
                    "Honest reporting of model uncertainty and prediction confidence"
                ]
            }
        ]
    },
    ModelStage.MODEL_EXPLANATION: {
        "title": "Model Explanation",
        "description": """
            Understand how your model makes decisions with interactive visualisations and detailed analysis:

            - **Feature Analysis**: Discover which features impact your model most using SHAP values and ALE plots
            - **Individual Predictions**: Analyse test cases with detailed breakdowns using waterfall plots and feature contributions
            - **What-If Analysis**: Test different scenarios by changing feature values and see how predictions change
            - **Fairness Analysis**: Check for potential bias across different demographic groups with statistical metrics
            - **Limitations & Recommendations**: Learn your model's strengths and potential improvement areas

            These tools help you build trust in your model by making its behavior transparent and understandable!
        """,
        "requirements": [
            {
                "title": "Feature Analysis & Importance",
                "items": [
                    "Review SHAP values to identify the most influential features",
                    "Explore feature impact with Accumulated Local Effects (ALE) plots"
                ]
            },
            {
                "title": "Individual Prediction Analysis",
                "items": [
                    "Select specific samples from your test dataset to analyse",
                    "Compare predicted vs actual values with detailed metrics",
                    "Visualize feature contributions with interactive waterfall plots",
                    "View detailed breakdown of how each feature affects predictions"
                ]
            },
            {
                "title": "What-If Analysis & Scenarios",
                "items": [
                    "Experiment by entering custom feature values to see real-time predictions",
                    "Save multiple scenarios to compare different feature configurations",
                    "Load samples from your test dataset as starting points",
                    "Generate detailed comparisons between scenarios to understand feature impact"
                ]
            },
            {
                "title": "Fairness Evaluation",
                "items": [
                    "Analyse model fairness across protected attributes using statistical metrics",
                    "Identify potential bias with demographic parity and equalized odds measures",
                    "Compare performance across different demographic groups",
                    "Visualize fairness metrics with interactive bar charts"
                ]
            },
            {
                "title": "Limitations & Recommendations",
                "items": [
                    "Review automatic evaluation of model strengths and weaknesses",
                    "Receive specific recommendations for model improvement",
                    "Understand model complexity and potential overfitting issues",
                    "Get insights on potential data quality and feature importance concerns"
                ]
            }
        ],
        "ethical_considerations": [
            {
                "title": "Responsible Model Deployment",
                "items": [
                    "Monitor fairness metrics like demographic parity and equalized odds",
                    "Check for potentially biased predictions across protected groups",
                    "Compare error rates and prediction distributions across different demographics",
                    "Implement specific bias mitigation strategies when issues are detected"
                ]
            },
            {
                "title": "Transparency & Interpretability",
                "items": [
                    "Provide both technical and accessible explanations with visual guides",
                    "Include detailed interpretations for all visualization types",
                    "Present feature importance and contributions in understandable ways",
                    "Show confidence levels and uncertainty in predictions and explanations"
                ]
            },
            {
                "title": "Appropriate Context",
                "items": [
                    "Clearly communicate model limitations and constraints",
                    "Explain feature interactions and their real-world meaning",
                    "Document reasoning behind fairness metrics and thresholds",
                    "Provide adequate warnings for potentially problematic predictions"
                ]
            },
            {
                "title": "Practical Impact Assessment",
                "items": [
                    "Consider how model decisions affect different stakeholders",
                    "Evaluate real-world consequences of false positives and negatives",
                    "Provide actionable recommendations for improvement",
                    "Highlight edge cases and rare events that might be overlooked"
                ]
            },
            {
                "title": "Continuous Monitoring",
                "items": [
                    "Implement processes to track explanation quality over time",
                    "Validate explanation accuracy against ground truth when available",
                    "Monitor for shifts in feature importance that might indicate drift",
                    "Regularly revisit fairness metrics as data and stakeholders evolve"
                ]
            }
        ]
    }
}