ETHICAL_CONSIDERATIONS.md

Ethical Considerations for NTSB Aviation Accident Analysis

Table of Contents

• Privacy and Data Anonymization
• Bias in Machine Learning Models
• Responsible AI Practices
• Regulatory Compliance
• Transparency Requirements
• Ethical Implications of Predictions
• Model Fairness Evaluation
• Stakeholder Engagement

---

Privacy and Data Anonymization

Current NTSB Data Privacy Status

The NTSB aviation accident database is public record under the Freedom of Information Act (FOIA). However, this doesn't eliminate ethical considerations:

• Names of deceased: Published in accident reports (next of kin notified before release)
• Pilot certificate numbers: May be included in investigative materials
• Aircraft tail numbers (N-numbers): Public FAA registry data
• Narrative text: May contain identifiable information about pilots, passengers, witnesses
• Locations: Precise GPS coordinates of accident sites

Privacy Principles

Despite public record status, we adhere to ethical privacy principles:

1. Minimization: Only process data necessary for safety research
2. Purpose Limitation: Use data solely for stated safety research purposes
3. Data Security: Protect data from unauthorized access or misuse
4. Respectful Handling: Treat sensitive information (fatalities) with dignity

---

Anonymization Strategies

Remove PII from Narratives

import re
from typing import Optional

def anonymize_narratives(text: str) -> str:
    """
    Remove personally identifiable information from accident narratives.

    Args:
        text: Raw narrative text

    Returns:
        Anonymized text
    """
    if not text or not isinstance(text, str):
        return text

    # Remove names (simplified - use NER for production)
    # Pattern: Capital letter followed by lowercase, then space, then Capital letter
    text = re.sub(r'\b[A-Z][a-z]+ [A-Z][a-z]+\b', '[NAME]', text)

    # Remove N-numbers (aircraft registration: N12345, N1234A, N123AB)
    text = re.sub(r'\bN\d{1,5}[A-Z]{0,2}\b', '[AIRCRAFT-ID]', text)

    # Remove phone numbers (multiple formats)
    text = re.sub(r'\b\d{3}[-.\s]?\d{3}[-.\s]?\d{4}\b', '[PHONE]', text)
    text = re.sub(r'\(\d{3}\)\s?\d{3}[-.\s]?\d{4}\b', '[PHONE]', text)

    # Remove email addresses
    text = re.sub(
        r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b',
        '[EMAIL]',
        text
    )

    # Remove addresses (street numbers + street names)
    text = re.sub(r'\b\d+\s+[A-Z][a-z]+\s+(Street|St|Avenue|Ave|Road|Rd|Drive|Dr|Lane|Ln|Boulevard|Blvd)\b', '[ADDRESS]', text, flags=re.IGNORECASE)

    # Remove Social Security Numbers
    text = re.sub(r'\b\d{3}-\d{2}-\d{4}\b', '[SSN]', text)

    return text


# Example usage
narrative = """
Pilot John Smith (555-123-4567) was flying aircraft N12345 from
123 Main Street when the accident occurred. Contact jane.doe@email.com
for more information.
"""

anonymized = anonymize_narratives(narrative)
print(anonymized)
# Output: "Pilot [NAME] ([PHONE]) was flying aircraft [AIRCRAFT-ID] from
#          [ADDRESS] when the accident occurred. Contact [EMAIL] for more information."

Advanced NER-Based Anonymization

import spacy
from typing import List, Tuple

# Load spaCy NER model
nlp = spacy.load("en_core_web_sm")

def advanced_anonymization(text: str) -> Tuple[str, List[dict]]:
    """
    Use Named Entity Recognition for comprehensive anonymization.

    Args:
        text: Raw narrative text

    Returns:
        Tuple of (anonymized_text, redactions_list)
    """
    doc = nlp(text)

    redactions = []
    anonymized_text = text

    # Entity types to redact
    redact_entities = {'PERSON', 'ORG', 'GPE', 'LOC', 'FAC'}

    for ent in reversed(doc.ents):  # Reverse to maintain indices
        if ent.label_ in redact_entities:
            # Log redaction for audit
            redactions.append({
                'text': ent.text,
                'type': ent.label_,
                'start': ent.start_char,
                'end': ent.end_char
            })

            # Replace with placeholder
            placeholder = f"[{ent.label_}]"
            anonymized_text = (
                anonymized_text[:ent.start_char] +
                placeholder +
                anonymized_text[ent.end_char:]
            )

    return anonymized_text, redactions

# Example
narrative = "Pilot John Smith from Cessna Aircraft Company departed from Los Angeles International Airport."
anon_text, redacted = advanced_anonymization(narrative)
print(anon_text)
# Output: "Pilot [PERSON] from [ORG] departed from [FAC]."

---

GDPR and CCPA Considerations

Though NTSB data is U.S. public domain, if serving European or California users, comply with:

GDPR (General Data Protection Regulation)

• Right to Access: Provide users their data on request (user accounts, query logs)
• Right to Deletion: Remove user accounts and associated data
• Data Minimization: Only collect necessary data
• Purpose Limitation: Use data only for stated purposes
• Consent: Obtain explicit consent for data collection (analytics, cookies)

CCPA (California Consumer Privacy Act)

• Right to Know: Disclose what personal information is collected
• Right to Delete: Honor deletion requests
• Right to Opt-Out: Allow users to opt out of data sale (we don't sell data)
• Non-Discrimination: Don't penalize users for exercising privacy rights

Implementation

class PrivacyManager:
    """Handle GDPR/CCPA compliance for user data"""

    def export_user_data(self, user_id: str) -> dict:
        """GDPR Article 15: Right to Access"""
        return {
            'user_id': user_id,
            'queries': self.get_user_queries(user_id),
            'settings': self.get_user_settings(user_id),
            'created_at': self.get_user_created_date(user_id)
        }

    def delete_user_data(self, user_id: str) -> bool:
        """GDPR Article 17: Right to Erasure"""
        # Delete user account
        self.delete_user_account(user_id)
        # Anonymize query logs (keep for analytics, remove PII)
        self.anonymize_query_logs(user_id)
        # Delete sessions
        self.delete_user_sessions(user_id)
        return True

    def get_consent_status(self, user_id: str) -> dict:
        """Check user consent for various processing activities"""
        return {
            'analytics': True,
            'marketing': False,
            'third_party_sharing': False
        }

---

Bias in Machine Learning Models

Machine learning models trained on historical data can perpetuate or amplify biases. Aviation accident data has several bias sources:

Types of Bias in Aviation Data

1. Reporting Bias

Definition: Not all accidents are reported equally.

• Commercial aviation: Near 100% reporting (FAA Part 121)
• General aviation: Lower reporting rate, especially for minor incidents
• International accidents: May not be in NTSB database
• Homebuilt aircraft: Underreported due to lower regulatory scrutiny

Impact: Models trained on this data may underestimate GA accident rates.

Mitigation:

• Weight training samples by aircraft category
• Supplement with FAA ASRS (voluntary reports) for near-misses
• Acknowledge limitations in model documentation

2. Demographic Bias

Definition: Underrepresentation of certain pilot demographics.

• Gender: 93% of pilots are male (FAA statistics)
• Age: Older pilots overrepresented in GA accidents (more GA pilots are older)
• Race/Ethnicity: Limited demographic data in NTSB database
• Socioeconomic: Private pilots skew higher income

Impact: Models may have lower accuracy for underrepresented groups.

Mitigation:

• Report performance metrics separately by demographic group
• Use stratified sampling to balance training data
• Acknowledge performance disparities in model cards

3. Temporal Bias

Definition: Accident characteristics change over time.

• 1960s-1970s: Different aircraft technology, safety standards
• 1980s-1990s: Introduction of TCAS, GPWS
• 2000s-2020s: Glass cockpits, NextGen ATC, improved training

Impact: Models trained on old data may not generalize to modern aviation.

Mitigation:

• Time-based train/test splits (train on 1962-2015, test on 2016-2024)
• Periodic model retraining (annual)
• Domain adaptation techniques

4. Geographic Bias

Definition: Rural accidents may be underreported or have less thorough investigations.

• Urban airports: More witnesses, better documentation
• Remote areas: Delayed discovery, limited evidence
• International waters: Jurisdictional challenges

Impact: Models may underperform for remote location accidents.

Mitigation:

• Geographic stratification in validation sets
• Sensitivity analysis: performance by location type

5. Severity Bias

Definition: Fatal accidents receive more investigation resources.

• Fatal accidents: Average 50+ pages of documentation
• Non-fatal incidents: May have 2-5 pages
• NTSB "Major" investigations: Team of 10+ investigators for weeks

Impact: More features available for fatal accidents, leading to better model performance.

Mitigation:

• Separate models for fatal vs non-fatal
• Feature parity: ensure similar feature availability

---

Bias Detection Framework

Aequitas Library

from aequitas.group import Group
from aequitas.bias import Bias
from aequitas.fairness import Fairness
import pandas as pd

def detect_model_bias(df: pd.DataFrame, predictions: list, protected_attributes: list):
    """
    Detect bias in model predictions using Aequitas.

    Args:
        df: DataFrame with demographic attributes
        predictions: Model predictions (binary: 0 = non-fatal, 1 = fatal)
        protected_attributes: List of sensitive attributes (e.g., ['crew_sex', 'crew_age_group'])

    Returns:
        Tuple of (bias_df, fairness_df)
    """
    # Prepare data
    df_eval = df.copy()
    df_eval['score'] = predictions  # Model predictions
    df_eval['label_value'] = (df['inj_tot_f'] > 0).astype(int)  # Ground truth

    # Run Aequitas analysis
    g = Group()
    xtab, _ = g.get_crosstabs(df_eval, protected_attributes)

    # Bias metrics
    b = Bias()
    bias_df = b.get_disparity_predefined_groups(
        xtab,
        original_df=df_eval,
        ref_groups_dict={
            'crew_sex': 'Male',  # Reference group
            'crew_age_group': '40-50'
        }
    )

    # Fairness assessment
    f = Fairness()
    fairness_df = f.get_group_value_fairness(bias_df)

    # Identify biased groups
    biased_metrics = bias_df[
        (bias_df['FPR_disparity'] < 0.8) |  # False Positive Rate disparity
        (bias_df['FPR_disparity'] > 1.2) |
        (bias_df['FNR_disparity'] < 0.8) |  # False Negative Rate disparity
        (bias_df['FNR_disparity'] > 1.2)
    ]

    print("=" * 80)
    print("BIAS DETECTION RESULTS")
    print("=" * 80)
    print(f"\nProtected Attributes: {', '.join(protected_attributes)}")
    print(f"\nBiased Groups Found: {len(biased_metrics)}")

    if len(biased_metrics) > 0:
        print("\n⚠️ Bias Detected:")
        for _, row in biased_metrics.iterrows():
            print(f"  {row['attribute_name']}={row['attribute_value']}: "
                  f"FPR_disparity={row['FPR_disparity']:.2f}, "
                  f"FNR_disparity={row['FNR_disparity']:.2f}")
    else:
        print("\n✅ No significant bias detected")

    return bias_df, fairness_df

# Example usage
df = pd.read_csv('data/avall-events.csv')
predictions = model.predict(X_test)
bias_df, fairness_df = detect_model_bias(df, predictions, ['crew_sex', 'crew_age_group'])

---

Fairness Metrics

Demographic Parity

Definition: P(ŷ=1 | A=0) = P(ŷ=1 | A=1)

Prediction rate should be equal across groups.

def demographic_parity(y_pred, protected_attribute):
    """Check if prediction rates are equal across groups"""
    groups = protected_attribute.unique()
    rates = {}
    for group in groups:
        mask = protected_attribute == group
        rates[group] = y_pred[mask].mean()

    # Check disparity
    max_rate = max(rates.values())
    min_rate = min(rates.values())
    disparity = max_rate / min_rate

    return {
        'rates': rates,
        'disparity': disparity,
        'is_fair': 0.8 <= disparity <= 1.2  # 80% rule
    }

Equalized Odds

Definition: TPR and FPR equal across groups.

from sklearn.metrics import confusion_matrix

def equalized_odds(y_true, y_pred, protected_attribute):
    """Check if TPR and FPR are equal across groups"""
    groups = protected_attribute.unique()
    metrics = {}

    for group in groups:
        mask = protected_attribute == group
        tn, fp, fn, tp = confusion_matrix(y_true[mask], y_pred[mask]).ravel()

        tpr = tp / (tp + fn) if (tp + fn) > 0 else 0
        fpr = fp / (fp + tn) if (fp + tn) > 0 else 0

        metrics[group] = {'TPR': tpr, 'FPR': fpr}

    return metrics

Predictive Parity

Definition: PPV (Precision) equal across groups.

def predictive_parity(y_true, y_pred, protected_attribute):
    """Check if PPV is equal across groups"""
    groups = protected_attribute.unique()
    ppvs = {}

    for group in groups:
        mask = protected_attribute == group
        tn, fp, fn, tp = confusion_matrix(y_true[mask], y_pred[mask]).ravel()
        ppv = tp / (tp + fp) if (tp + fp) > 0 else 0
        ppvs[group] = ppv

    return ppvs

---

Bias Mitigation Strategies

Pre-Processing: Re-weighting

from sklearn.utils.class_weight import compute_sample_weight

def mitigate_bias_reweighting(X, y, protected_attribute):
    """
    Assign higher weights to underrepresented groups.

    Args:
        X: Features
        y: Labels
        protected_attribute: Sensitive attribute

    Returns:
        Sample weights
    """
    # Compute class weights for protected attribute
    sample_weights = compute_sample_weight(
        class_weight='balanced',
        y=protected_attribute
    )

    return sample_weights

# Usage
weights = mitigate_bias_reweighting(X_train, y_train, X_train['crew_sex'])
model.fit(X_train, y_train, sample_weight=weights)

In-Processing: Fairness Constraints

from fairlearn.reductions import ExponentiatedGradient, DemographicParity
from sklearn.ensemble import RandomForestClassifier

def train_fair_model(X, y, protected_attribute):
    """
    Train model with fairness constraints.

    Args:
        X: Features
        y: Labels
        protected_attribute: Sensitive attribute

    Returns:
        Fair classifier
    """
    base_model = RandomForestClassifier(n_estimators=100, random_state=42)

    # Apply demographic parity constraint
    fair_model = ExponentiatedGradient(
        base_model,
        constraints=DemographicParity(),
        eps=0.05  # Tolerance for fairness violation
    )

    fair_model.fit(X, y, sensitive_features=protected_attribute)

    return fair_model

# Usage
fair_clf = train_fair_model(X_train, y_train, X_train['crew_sex'])

Post-Processing: Threshold Adjustment

def adjust_thresholds_per_group(y_true, y_scores, protected_attribute, target_fpr=0.10):
    """
    Adjust decision thresholds to equalize FPR across groups.

    Args:
        y_true: Ground truth labels
        y_scores: Model prediction scores
        protected_attribute: Sensitive attribute
        target_fpr: Target false positive rate

    Returns:
        Dictionary of thresholds per group
    """
    from sklearn.metrics import roc_curve

    groups = protected_attribute.unique()
    thresholds = {}

    for group in groups:
        mask = protected_attribute == group
        fpr, tpr, thresh = roc_curve(y_true[mask], y_scores[mask])

        # Find threshold closest to target FPR
        idx = np.argmin(np.abs(fpr - target_fpr))
        thresholds[group] = thresh[idx]

    return thresholds

# Usage
thresholds = adjust_thresholds_per_group(y_true, y_scores, protected_attr)

def fair_predict(x, group):
    """Apply group-specific threshold"""
    score = model.predict_proba(x)[1]
    threshold = thresholds[group]
    return 1 if score >= threshold else 0

---

Responsible AI Practices

Microsoft Responsible AI Framework

Six core principles guide our development:

1. Fairness: Treat all people equitably
2. Reliability & Safety: Perform consistently and safely
3. Privacy & Security: Be secure and respect privacy
4. Inclusiveness: Empower everyone
5. Transparency: Be understandable
6. Accountability: Take responsibility for AI systems

Implementation Checklist

• Document model limitations in model cards (see below)
• Provide confidence intervals for all predictions
• Explain predictions using SHAP/LIME (see MACHINE_LEARNING_APPLICATIONS.md)
• Conduct regular bias audits (quarterly for production models)
• Human oversight for high-stakes decisions (e.g., insurance rates, regulations)
• Appeal mechanism for predictions affecting individuals
• Regular model retraining (annual, or when data drift detected)
• Version control all models, data, and code
• Incident response plan for model failures or biased outcomes
• Ethical review board approval for new model deployments

---

Regulatory Compliance

FAA Regulations

Advisory Circulars (Proposed)

The FAA is developing guidance on AI/ML in aviation:

• AC 120-XXX (draft): "Machine Learning in Aviation Safety Applications"
  • Requires human-interpretable predictions
  • Mandates audit trails for all decisions
  • Prohibits fully autonomous safety determinations

Compliance Requirements

1. Human-in-the-Loop: No fully automated decisions on pilot certification, aircraft certification, or safety enforcement
2. Explainability: All predictions must be explainable to non-technical stakeholders
3. Audit Trail: Log all model predictions, inputs, and decision factors
4. Performance Monitoring: Continuously monitor model accuracy, detect drift
5. Bias Testing: Regular fairness audits across demographic groups

class FAA_Compliant_Predictor:
    """FAA-compliant ML predictor with full audit trail"""

    def __init__(self, model, explainer):
        self.model = model
        self.explainer = explainer
        self.audit_log = []

    def predict_with_explanation(self, X, user_id=None):
        """Make prediction with full audit trail"""
        import uuid
        from datetime import datetime

        prediction_id = str(uuid.uuid4())

        # Make prediction
        pred = self.model.predict(X)
        pred_proba = self.model.predict_proba(X)

        # Generate explanation
        explanation = self.explainer.shap_values(X)

        # Log for audit
        self.audit_log.append({
            'prediction_id': prediction_id,
            'timestamp': datetime.now().isoformat(),
            'user_id': user_id,
            'input': X.to_dict(orient='records')[0],
            'prediction': int(pred[0]),
            'confidence': float(pred_proba[0][1]),
            'explanation': explanation.tolist()
        })

        return {
            'prediction': pred[0],
            'confidence': pred_proba[0][1],
            'explanation': explanation,
            'prediction_id': prediction_id
        }

    def export_audit_log(self, path='audit_log.json'):
        """Export audit log for FAA review"""
        import json
        with open(path, 'w') as f:
            json.dump(self.audit_log, f, indent=2)

---

NTSB Data Usage Policies

• Data is public domain: No copyright restrictions
• Proper attribution required: Cite NTSB as source
• Commercial use: Allowed (public domain data)
• Ongoing investigations: Respect confidentiality during active investigations
• Accuracy: Don't misrepresent NTSB findings
• Ethics: Use data responsibly for safety improvement

---

Transparency Requirements

Model Cards (Mitchell et al., 2019)

Document all production models with standardized "model cards":

# Model Card: Aviation Accident Severity Predictor

## Model Details
- **Developed by**: NTSB Analytics Team
- **Model date**: January 2025
- **Model version**: 2.3.1
- **Model type**: XGBoost classifier (500 trees, max_depth=6)
- **License**: MIT
- **Contact**: analytics@ntsb.gov

## Intended Use
- **Primary use**: Predict accident severity (no injury, minor, serious, fatal) for safety research
- **Intended users**: Aviation safety researchers, FAA analysts, academic researchers
- **Out-of-scope use cases**:
  - ❌ Pilot certification decisions (use only as advisory input, not deterministic)
  - ❌ Insurance underwriting without human review
  - ❌ Criminal investigations or litigation
  - ❌ Real-time flight safety (model not certified for real-time use)

## Training Data
- **Dataset**: NTSB aviation accidents (1962-2024)
- **Size**: 50,000 accidents (40,000 train, 10,000 test)
- **Geographic coverage**: United States (50 states + territories)
- **Temporal coverage**: 62 years
- **Data splits**:
  - Train: 1962-2019 (80%)
  - Validation: 2020-2022 (10%)
  - Test: 2023-2024 (10%)

## Evaluation Data
- **Holdout set**: 10,000 accidents (2020-2024)
- **Test set performance**:
  - Overall Accuracy: 89.2%
  - F1 Score: 0.91 (macro-average)
  - Precision: 0.89
  - Recall: 0.93
- **Cross-validation**: 5-fold CV, mean F1 = 0.90 ± 0.02

## Performance Metrics
| Class | Precision | Recall | F1-Score | Support |
|-------|-----------|--------|----------|---------|
| No Injury | 0.92 | 0.89 | 0.90 | 3,200 |
| Minor | 0.87 | 0.88 | 0.87 | 2,500 |
| Serious | 0.85 | 0.90 | 0.87 | 1,800 |
| Fatal | 0.91 | 0.96 | 0.94 | 2,500 |
| **Weighted Avg** | **0.89** | **0.91** | **0.90** | **10,000** |

## Ethical Considerations
- **Bias detected**: Slight underprediction for crew age 65+ (5% lower recall)
  - **Mitigation**: Post-processing threshold adjustment for older pilots
- **Demographic coverage**:
  - Gender: 93% male, 7% female (reflects pilot population)
  - Age: 18-85 years (median: 52)
  - Aircraft type: GA (78%), Commercial (15%), Military (7%)
- **Limitations**:
  - Lower accuracy for rare aircraft types (<50 examples in training set)
  - Performance degrades for international accidents (not in training data)
  - Model has not been validated on experimental/homebuilt aircraft

## Caveats and Recommendations
- **Predictions should not be sole basis for decisions**: Always combine with human expert judgment
- **Human expert review required for**:
  - High-stakes decisions (pilot licensing, aircraft certification)
  - Cases where model confidence <80%
  - Novel aircraft types or operations not in training data
- **Model should be retrained annually**: To capture evolving aircraft technology and safety practices
- **Monitor for data drift**: Alert if input distribution changes significantly
- **Confidence thresholds**:
  - High confidence (>90%): Safe for automated flagging
  - Medium confidence (70-90%): Recommend human review
  - Low confidence (<70%): Do not use for decision-making

## Changelog
- **v2.3.1 (Jan 2025)**: Added post-processing bias mitigation for age groups
- **v2.3.0 (Dec 2024)**: Retrained on 2023-2024 data
- **v2.2.0 (Jun 2024)**: Initial production release

---

Datasheets for Datasets (Gebru et al., 2018)

Document datasets with comprehensive "datasheets":

# Datasheet: NTSB Aviation Accident Database (1962-2024)

## Motivation
- **Purpose**: Comprehensive record of aviation accidents for safety research
- **Creator**: National Transportation Safety Board (NTSB)
- **Funding**: U.S. Federal Government appropriations

## Composition
- **Instances**: 50,000+ accident records (1962-2024)
- **Data types**: Structured (events, aircraft, crew) + unstructured (narratives)
- **Labels**: Accident severity, probable cause codes, phase of operation
- **Missing data**: ~15% of records have missing geographic coordinates
- **Confidential information**: None (public domain data)

## Collection Process
- **Acquisition**: NTSB accident investigators collect data on-site
- **Timeframe**: Ongoing since 1962
- **Sampling strategy**: Census (all reportable accidents)
- **Data collection instruments**: Investigation reports, witness interviews, flight data recorders

## Preprocessing
- **Raw data**: Microsoft Access databases (.mdb format)
- **Preprocessing**: Extraction to CSV, coordinate validation, duplicate removal
- **Tools**: mdbtools, Python pandas, DuckDB

## Uses
- **Prior use**: Academic research, FAA rulemaking, aircraft design improvements
- **Future use**: Machine learning, trend analysis, causal inference
- **Impact**: Improvements in aviation safety regulations and aircraft design

## Distribution
- **Availability**: Public domain (NTSB website, this GitHub repository)
- **License**: Public domain (U.S. government work)
- **Restrictions**: None (public data)

## Maintenance
- **Updates**: Monthly (avall.mdb database)
- **Versioning**: avall.mdb (current), Pre2008.mdb (archived), PRE1982.MDB (archived)
- **Errata**: Corrections published in MDB Release Notes

---

Ethical Implications of Predictions

High-Stakes Decisions

Model predictions may influence decisions with significant consequences:

Insurance Rate Setting

• Risk: Unfair premiums based on biased predictions
• Mitigation: Human underwriter review, appeal process, bias audits
• Transparency: Disclose model usage, provide explanations

Pilot Licensing

• Risk: Denying certification based on algorithmic prediction
• Mitigation: FAA regulation prohibits fully automated decisions
• Requirement: Human flight examiner makes final determination

Regulatory Action

• Risk: Grounding aircraft or revoking certificates based on model output
• Mitigation: Predictions inform, but do not determine, regulatory actions
• Oversight: FAA review board approval required

Legal Proceedings

• Risk: Model predictions used as evidence in litigation
• Status: Generally not admissible as sole evidence
• Requirement: Expert witness testimony to interpret model outputs

---

Potential Harms

False Positives

Definition: Model predicts high severity when actual outcome is minor.

• Consequence: Unnecessary regulatory burden, higher insurance costs
• Mitigation: Optimize for precision, use conservative thresholds

False Negatives

Definition: Model predicts low severity when actual outcome is catastrophic.

• Consequence: Underestimating risk, inadequate safety measures
• Mitigation: Optimize for recall (sensitivity), bias toward safety

Discrimination

Definition: Unfair treatment of demographic groups.

• Consequence: Pilots from certain groups face higher scrutiny or costs
• Mitigation: Fairness audits, bias mitigation techniques, transparent reporting

Privacy Invasion

Definition: Revealing sensitive information about individuals.

• Consequence: Reputational harm, emotional distress for families
• Mitigation: Anonymization, respectful handling of sensitive data

---

Model Fairness Evaluation

Fairlearn Integration

from fairlearn.metrics import MetricFrame, selection_rate
from sklearn.metrics import accuracy_score, precision_score, recall_score

def evaluate_fairness(y_true, y_pred, sensitive_features):
    """
    Comprehensive fairness evaluation using Fairlearn.

    Args:
        y_true: Ground truth labels
        y_pred: Model predictions
        sensitive_features: DataFrame with sensitive attributes

    Returns:
        MetricFrame with per-group metrics
    """
    # Define metrics
    metric_frame = MetricFrame(
        metrics={
            'accuracy': accuracy_score,
            'precision': precision_score,
            'recall': recall_score,
            'selection_rate': selection_rate
        },
        y_true=y_true,
        y_pred=y_pred,
        sensitive_features=sensitive_features
    )

    # Display overall metrics
    print("=" * 80)
    print("FAIRNESS EVALUATION")
    print("=" * 80)
    print("\nOverall Metrics:")
    print(metric_frame.overall)

    # Display by group
    print("\nMetrics by Group:")
    print(metric_frame.by_group)

    # Calculate disparities
    print("\nDisparities (max - min):")
    print(metric_frame.difference())

    # Calculate ratios
    print("\nDisparities (max / min):")
    print(metric_frame.ratio())

    return metric_frame

# Example usage
sensitive_features = pd.DataFrame({
    'crew_sex': df_test['crew_sex'],
    'crew_age_group': pd.cut(df_test['crew_age'], bins=[0, 30, 50, 65, 100], labels=['<30', '30-50', '50-65', '65+'])
})

metric_frame = evaluate_fairness(y_test, y_pred, sensitive_features)

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Stakeholder Engagement

Key Stakeholders

1. Pilots and Flight Crews: Affected by predictions about accident risk
2. Aviation Safety Organizations: NTSB, FAA, Flight Safety Foundation
3. Regulators: FAA rulemaking officials, NTSB investigators
4. Aircraft Manufacturers: Boeing, Airbus, Cessna, Piper
5. Insurance Companies: Use risk models for underwriting
6. General Public: Beneficiaries of safer aviation system

Engagement Strategies

Advisory Board

• Composition: 10-15 members representing diverse stakeholder groups
• Meeting Frequency: Quarterly
• Responsibilities:
  • Review model development plans
  • Provide feedback on ethical considerations
  • Approve major model changes
  • Advise on fairness metrics

Public Comment Periods

• Duration: 30-60 days for major model changes
• Mechanism: GitHub Issues, email, public meetings
• Consideration: All comments reviewed and addressed in writing

Transparency Reports

• Frequency: Annual
• Contents:
  • Model performance metrics
  • Bias audit results
  • Incident reports (model failures)
  • Dataset updates
  • Stakeholder feedback summary

User Surveys and Feedback

• Frequency: Continuous (embedded in applications)
• Questions:
  • "Was this prediction helpful?"
  • "Do you trust this explanation?"
  • "Did you encounter any issues?"
• Analysis: Quarterly review, action items for product team

Pilot Testing with Stakeholders

• Beta Program: 50-100 users from each stakeholder group
• Duration: 3-6 months before public release
• Feedback Mechanism: Interviews, surveys, usage analytics

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Summary

Ethical considerations are central to responsible development of aviation safety analytics. By prioritizing:

• Privacy: Anonymization, GDPR/CCPA compliance
• Fairness: Bias detection, mitigation, and continuous monitoring
• Transparency: Model cards, datasheets, explainable AI
• Accountability: Human oversight, audit trails, incident response
• Stakeholder Engagement: Advisory boards, public comment, transparency reports

We ensure that AI systems built on NTSB data improve aviation safety equitably and responsibly.

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Document Version: 1.0 Last Updated: November 2025 Target Audience: ML engineers, ethicists, policymakers, stakeholders Related Documents: MACHINE_LEARNING_APPLICATIONS.md, DATA_QUALITY_STRATEGY.md, RESEARCH_OPPORTUNITIES.md