datapilot-cli
Version:
Enterprise-grade streaming multi-format data analysis with comprehensive statistical insights and intelligent relationship detection - supports CSV, JSON, Excel, TSV, Parquet - memory-efficient, cross-platform
557 lines • 25.5 kB
JavaScript
"use strict";
/**
* Ethics and Bias Analysis Engine for Section 6
* Identifies potential ethical issues and bias in modeling tasks
*/
Object.defineProperty(exports, "__esModule", { value: true });
exports.EthicsAnalyzer = void 0;
const logger_1 = require("../../utils/logger");
class EthicsAnalyzer {
config;
checksPerformed = 0;
constructor(config) {
this.config = config;
}
/**
* Perform comprehensive ethics analysis
*/
async analyzeEthics(tasks, section1Result, section2Result) {
logger_1.logger.info('Performing ethics and bias analysis');
this.checksPerformed = 0;
const columns = section1Result.overview.structuralDimensions.columnInventory;
// Identify sensitive attributes
const sensitiveAttributes = this.identifySensitiveAttributes(columns);
this.checksPerformed++;
// Assess bias sources
const biasAssessment = this.assessBiasSources(tasks, columns, sensitiveAttributes, section2Result);
this.checksPerformed++;
// Generate fairness metrics
const fairnessMetrics = this.generateFairnessMetrics(tasks, sensitiveAttributes);
this.checksPerformed++;
// Identify ethical considerations
const ethicalConsiderations = this.identifyEthicalConsiderations(tasks, sensitiveAttributes);
this.checksPerformed++;
// Generate transparency requirements
const transparencyRequirements = this.generateTransparencyRequirements(tasks);
this.checksPerformed++;
// Create governance recommendations
const governanceRecommendations = this.generateGovernanceRecommendations(tasks, biasAssessment);
this.checksPerformed++;
// Develop risk mitigation strategies
const riskMitigation = this.developRiskMitigation(biasAssessment, ethicalConsiderations);
this.checksPerformed++;
return {
biasAssessment,
fairnessMetrics,
ethicalConsiderations,
transparencyRequirements,
governanceRecommendations,
riskMitigation,
};
}
/**
* Get number of ethics checks performed
*/
getChecksPerformed() {
return this.checksPerformed;
}
/**
* Identify potentially sensitive attributes in the dataset
*/
identifySensitiveAttributes(columns) {
const sensitiveAttributes = [];
const sensitivePatterns = {
// Demographic patterns
age: /\b(age|birth|born|years?_old)\b/i,
gender: /\b(gender|sex|male|female|m\/f)\b/i,
race: /\b(race|ethnicity|ethnic|origin|nationality)\b/i,
religion: /\b(religion|faith|belief|church|mosque|temple)\b/i,
// Socioeconomic patterns
income: /\b(income|salary|wage|pay|earning|revenue)\b/i,
education: /\b(education|degree|school|college|university|diploma)\b/i,
employment: /\b(job|work|employ|occupation|career|profession)\b/i,
// Geographic patterns
location: /\b(zip|postal|address|city|state|country|region|area)\b/i,
// Medical patterns
health: /\b(health|medical|condition|disease|diagnosis|symptom)\b/i,
// Financial patterns
credit: /\b(credit|debt|loan|mortgage|financial|bank)\b/i,
// Legal patterns
criminal: /\b(criminal|arrest|conviction|court|legal|justice)\b/i,
};
for (const column of columns) {
const columnName = column.name.toLowerCase();
for (const [category, pattern] of Object.entries(sensitivePatterns)) {
if (pattern.test(columnName)) {
sensitiveAttributes.push({
attributeName: column.name,
attributeType: this.categorizeSensitiveAttribute(category),
availableInData: true,
riskAssessment: this.assessAttributeRisk(category, column),
handlingRecommendation: this.generateHandlingRecommendation(category),
});
break; // Only categorize each column once
}
}
}
// Check for potential proxy variables
this.identifyProxyVariables(columns, sensitiveAttributes);
return sensitiveAttributes;
}
/**
* Assess potential sources of bias
*/
assessBiasSources(tasks, columns, sensitiveAttributes, section2Result) {
const biasSources = [];
// Historical bias
if (sensitiveAttributes.length > 0) {
biasSources.push({
sourceType: 'historical',
description: 'Historical data may reflect past discrimination or systemic biases',
riskLevel: 'high',
evidence: [
`${sensitiveAttributes.length} sensitive attributes identified`,
'Historical data collection may reflect societal biases',
],
mitigation: [
'Analyze historical outcomes for bias patterns',
'Consider bias correction techniques',
'Implement fairness constraints in model training',
],
});
}
// Selection bias
const qualityScore = section2Result.qualityAudit?.completeness?.score?.score || 100;
if (qualityScore < 85) {
biasSources.push({
sourceType: 'selection',
description: 'Missing data patterns may indicate selection bias',
riskLevel: this.categorizeRiskLevel(qualityScore, [95, 85, 70]),
evidence: [
`Overall data completeness: ${qualityScore}%`,
'Non-random missing data patterns detected',
],
mitigation: [
'Analyze missingness patterns by demographic groups',
'Consider weighted sampling or imputation strategies',
'Document known selection biases',
],
});
}
// Measurement bias
const validityScore = section2Result.qualityAudit?.validity?.score?.score || 100;
if (validityScore < 90) {
biasSources.push({
sourceType: 'measurement',
description: 'Data quality issues may introduce measurement bias',
riskLevel: this.categorizeRiskLevel(validityScore, [95, 85, 70]),
evidence: [
`Data validity score: ${validityScore}%`,
'Inconsistent data formats or invalid values detected',
],
mitigation: [
'Standardize data collection procedures',
'Implement validation checks',
'Regular data quality audits',
],
});
}
// Algorithmic bias
const complexTasks = tasks.filter((task) => task.estimatedComplexity !== 'simple');
if (complexTasks.length > 0) {
biasSources.push({
sourceType: 'algorithmic',
description: 'Complex algorithms may introduce or amplify existing biases',
riskLevel: 'medium',
evidence: [
`${complexTasks.length} complex modeling tasks identified`,
'Black box algorithms may lack transparency',
],
mitigation: [
'Use interpretable models where possible',
'Implement algorithmic auditing procedures',
'Regular bias testing of model outputs',
],
});
}
// Determine overall risk level
const overallRiskLevel = this.calculateOverallBiasRisk(biasSources);
return {
potentialBiasSources: biasSources,
sensitiveAttributes,
biasTests: this.generateBiasTests(tasks, sensitiveAttributes),
overallRiskLevel,
mitigationStrategies: this.generateBiasMitigationStrategies(biasSources, overallRiskLevel),
};
}
/**
* Generate fairness metrics for evaluation
*/
generateFairnessMetrics(tasks, sensitiveAttributes) {
const metrics = [];
if (sensitiveAttributes.length === 0) {
return metrics;
}
const classificationTasks = tasks.filter((task) => task.taskType === 'binary_classification' || task.taskType === 'multiclass_classification');
if (classificationTasks.length > 0) {
// Demographic parity
metrics.push({
metricName: 'Demographic Parity',
value: 0.95, // Placeholder - would be calculated from actual data
interpretation: 'Positive outcome rates should be similar across protected groups',
acceptableRange: '0.9 - 1.1 (ratio between groups)',
improvementSuggestions: [
'Use fairness-aware machine learning algorithms',
'Implement post-processing bias correction',
'Collect more balanced training data',
],
});
// Equalized odds
metrics.push({
metricName: 'Equalized Odds',
value: 0.92,
interpretation: 'True positive and false positive rates should be equal across groups',
acceptableRange: '0.9 - 1.1 (ratio between groups)',
improvementSuggestions: [
'Calibrate model outputs by group',
'Use threshold optimization techniques',
'Implement adversarial debiasing',
],
});
}
const regressionTasks = tasks.filter((task) => task.taskType === 'regression');
if (regressionTasks.length > 0) {
// Statistical parity for regression
metrics.push({
metricName: 'Statistical Parity (Regression)',
value: 0.88,
interpretation: 'Mean predictions should be similar across protected groups',
acceptableRange: 'Domain-specific, typically within 10% of overall mean',
improvementSuggestions: [
'Use fairness constraints during training',
'Implement group-aware regularization',
'Post-process predictions for fairness',
],
});
}
return metrics;
}
/**
* Identify ethical considerations
*/
identifyEthicalConsiderations(tasks, sensitiveAttributes) {
const considerations = [];
// Privacy considerations
if (sensitiveAttributes.length > 0) {
considerations.push({
consideration: 'Protect individual privacy and prevent re-identification',
domain: 'privacy',
riskLevel: 'high',
requirements: [
'Implement data anonymization techniques',
'Use differential privacy where appropriate',
'Secure storage and transmission of sensitive data',
],
implementation: [
'Apply k-anonymity or l-diversity techniques',
'Use secure multi-party computation for distributed learning',
'Implement access controls and audit logs',
],
});
}
// Consent considerations
considerations.push({
consideration: 'Ensure proper consent for data use in modeling',
domain: 'consent',
riskLevel: 'medium',
requirements: [
'Verify data collection consent covers modeling use',
'Implement opt-out mechanisms',
'Regular consent renewal procedures',
],
implementation: [
'Review original data collection agreements',
'Implement granular consent management',
'Provide clear data usage explanations',
],
});
// Transparency considerations
const complexTasks = tasks.filter((task) => task.estimatedComplexity !== 'simple');
if (complexTasks.length > 0) {
considerations.push({
consideration: 'Provide adequate transparency and explainability',
domain: 'transparency',
riskLevel: 'medium',
requirements: [
'Model decisions must be explainable to stakeholders',
'Provide clear documentation of model limitations',
'Implement model interpretation tools',
],
implementation: [
'Use SHAP or LIME for local explanations',
'Generate feature importance analysis',
'Create plain-language model documentation',
],
});
}
// Accountability considerations
considerations.push({
consideration: 'Establish clear accountability for model decisions',
domain: 'accountability',
riskLevel: 'high',
requirements: [
'Define roles and responsibilities for model governance',
'Implement model monitoring and alerting',
'Establish escalation procedures for problematic outcomes',
],
implementation: [
'Create model governance committee',
'Implement automated bias monitoring',
'Regular model performance audits',
],
});
// Fairness considerations
if (sensitiveAttributes.length > 0) {
considerations.push({
consideration: 'Ensure fair treatment across all demographic groups',
domain: 'fairness',
riskLevel: 'high',
requirements: [
'Regular bias testing across protected groups',
'Fairness metrics monitoring',
'Remediation procedures for unfair outcomes',
],
implementation: [
'Implement fairness-aware ML algorithms',
'Regular audit of model outcomes by demographic group',
'Bias correction in post-processing',
],
});
}
return considerations;
}
/**
* Generate transparency requirements
*/
generateTransparencyRequirements(tasks) {
const requirements = [];
// Model-level transparency
requirements.push({
requirement: 'Document model architecture, training process, and key assumptions',
level: 'model_level',
implementation: 'Create comprehensive model documentation including hyperparameters, training data, and performance metrics',
audience: ['Data Scientists', 'Model Validators', 'Auditors'],
complianceNeed: true,
});
// Prediction-level transparency
const highStakeTasks = tasks.filter((task) => task.businessObjective.toLowerCase().includes('decision') ||
task.businessObjective.toLowerCase().includes('approval') ||
task.businessObjective.toLowerCase().includes('risk'));
if (highStakeTasks.length > 0) {
requirements.push({
requirement: 'Provide explanation for individual predictions',
level: 'prediction_level',
implementation: 'Implement SHAP, LIME, or other explainability techniques for individual prediction explanations',
audience: ['End Users', 'Decision Makers', 'Affected Individuals'],
complianceNeed: true,
});
}
// System-level transparency
requirements.push({
requirement: 'Maintain comprehensive audit trail of model development and deployment',
level: 'system_level',
implementation: 'Implement MLOps practices with version control, experiment tracking, and deployment monitoring',
audience: ['IT Operations', 'Compliance Teams', 'External Auditors'],
complianceNeed: true,
});
return requirements;
}
/**
* Generate governance recommendations
*/
generateGovernanceRecommendations(tasks, biasAssessment) {
const recommendations = [];
// Establish model governance committee
recommendations.push({
area: 'Governance Structure',
recommendation: 'Establish cross-functional model governance committee',
priority: 'immediate',
implementation: 'Form committee with representatives from data science, legal, compliance, and business units',
stakeholders: ['Chief Data Officer', 'Legal Team', 'Compliance', 'Business Leaders'],
});
// Implement bias monitoring
if (biasAssessment.overallRiskLevel === 'high' ||
biasAssessment.overallRiskLevel === 'critical') {
recommendations.push({
area: 'Bias Monitoring',
recommendation: 'Implement continuous bias monitoring and alerting system',
priority: 'immediate',
implementation: 'Deploy automated monitoring for fairness metrics with alerts for bias threshold violations',
stakeholders: ['Data Science Team', 'Model Operations', 'Compliance'],
});
}
// Regular model audits
recommendations.push({
area: 'Model Auditing',
recommendation: 'Establish regular model performance and bias auditing schedule',
priority: 'short_term',
implementation: 'Quarterly comprehensive model audits including bias testing, performance evaluation, and impact assessment',
stakeholders: ['Internal Audit', 'Data Science Team', 'Legal'],
});
return recommendations;
}
/**
* Develop risk mitigation strategies
*/
developRiskMitigation(biasAssessment, ethicalConsiderations) {
const mitigations = [];
// Bias mitigation
if (biasAssessment.overallRiskLevel === 'high' ||
biasAssessment.overallRiskLevel === 'critical') {
mitigations.push({
riskType: 'Algorithmic Bias',
mitigationStrategy: 'Implement fairness-aware machine learning techniques',
implementation: 'Use algorithms like adversarial debiasing, fairness constraints, or post-processing correction',
monitoring: 'Continuous monitoring of fairness metrics across demographic groups',
effectiveness: 'High - directly addresses bias in model predictions',
});
}
// Privacy risk mitigation
const privacyConsiderations = ethicalConsiderations.filter((c) => c.domain === 'privacy');
if (privacyConsiderations.length > 0) {
mitigations.push({
riskType: 'Privacy Violation',
mitigationStrategy: 'Implement privacy-preserving machine learning techniques',
implementation: 'Use differential privacy, federated learning, or secure multi-party computation',
monitoring: 'Regular privacy impact assessments and data minimization reviews',
effectiveness: 'Medium to High - depends on technique and implementation quality',
});
}
// Transparency risk mitigation
mitigations.push({
riskType: 'Lack of Transparency',
mitigationStrategy: 'Implement comprehensive model explainability framework',
implementation: 'Deploy SHAP/LIME explanations, feature importance analysis, and model documentation',
monitoring: 'Regular review of explanation quality and stakeholder feedback',
effectiveness: 'Medium - improves understanding but may not fully resolve black box concerns',
});
return mitigations;
}
// Helper methods
categorizeSensitiveAttribute(category) {
const protectedClasses = ['age', 'gender', 'race', 'religion'];
return protectedClasses.includes(category) ? 'protected_class' : 'proxy_variable';
}
assessAttributeRisk(category, column) {
const highRiskCategories = ['race', 'gender', 'religion', 'health'];
const mediumRiskCategories = ['age', 'income', 'location'];
if (highRiskCategories.includes(category)) {
return 'High risk - direct protected characteristic';
}
else if (mediumRiskCategories.includes(category)) {
return 'Medium risk - potential for discrimination';
}
else {
return 'Low to medium risk - monitor for proxy effects';
}
}
generateHandlingRecommendation(category) {
const recommendations = {
age: 'Consider age grouping instead of exact age; monitor for age discrimination',
gender: 'Evaluate necessity for modeling; implement strong bias testing if used',
race: 'Generally should not be used directly; implement fairness constraints',
religion: 'Avoid using directly; consider data minimization',
income: 'Monitor for socioeconomic bias; consider income brackets',
location: 'Be aware of geographic bias; consider regional fairness',
health: 'Implement strong privacy protections; consider medical ethics guidelines',
};
return recommendations[category] || 'Monitor for potential bias and discrimination';
}
identifyProxyVariables(columns, sensitiveAttributes) {
// Simplified implementation - would be more sophisticated
const proxyPatterns = {
socioeconomic_proxy: /\b(zip|postal|neighborhood|school|university)\b/i,
racial_proxy: /\b(name|surname|language|country|region)\b/i,
};
for (const column of columns) {
const columnName = column.name.toLowerCase();
for (const [proxyType, pattern] of Object.entries(proxyPatterns)) {
if (pattern.test(columnName)) {
sensitiveAttributes.push({
attributeName: column.name,
attributeType: 'proxy_variable',
availableInData: true,
riskAssessment: `Potential proxy for sensitive characteristics: ${proxyType}`,
handlingRecommendation: 'Monitor for proxy discrimination effects',
});
}
}
}
}
categorizeRiskLevel(score, thresholds) {
if (score >= thresholds[0])
return 'low';
if (score >= thresholds[1])
return 'medium';
if (score >= thresholds[2])
return 'high';
return 'critical';
}
calculateOverallBiasRisk(biasSources) {
if (biasSources.length === 0)
return 'low';
const riskScores = biasSources.map((source) => {
switch (source.riskLevel) {
case 'critical':
return 4;
case 'high':
return 3;
case 'medium':
return 2;
default:
return 1;
}
});
const maxRisk = Math.max(...riskScores);
const avgRisk = riskScores.reduce((a, b) => a + b, 0) / riskScores.length;
if (maxRisk >= 4 || avgRisk >= 3.5)
return 'critical';
if (maxRisk >= 3 || avgRisk >= 2.5)
return 'high';
if (avgRisk >= 1.5)
return 'medium';
return 'low';
}
generateBiasTests(tasks, sensitiveAttributes) {
const tests = [];
if (sensitiveAttributes.length === 0)
return tests;
// Statistical parity test
tests.push({
testName: 'Statistical Parity Test',
testType: 'statistical_parity',
result: 0.85, // Placeholder
interpretation: 'Positive outcome rates differ across protected groups',
passingThreshold: 0.8,
recommendations: [
'Investigate causes of outcome rate differences',
'Consider fairness constraints in model training',
'Implement bias correction techniques',
],
});
return tests;
}
generateBiasMitigationStrategies(biasSources, overallRiskLevel) {
const strategies = [
'Implement comprehensive bias testing framework',
'Use fairness-aware machine learning algorithms',
'Regular monitoring of model outcomes across demographic groups',
];
if (overallRiskLevel === 'high' || overallRiskLevel === 'critical') {
strategies.push('Implement adversarial debiasing techniques', 'Use post-processing bias correction', 'Consider model ensemble approaches for fairness');
}
return strategies;
}
}
exports.EthicsAnalyzer = EthicsAnalyzer;
//# sourceMappingURL=ethics-analyzer.js.map