MCP-fMRI: Ethical Analysis of Mathematical Abilities Using Generative AI

Performance analysis in mathematical skills with emphasis on gender similarities using fMRI neuroimaging data and generative AI techniques - focused on Japanese populations

🆕 NEW: Comprehensive Economic Bias Detection for Economics Research

🎯 Research Objectives

This package provides tools for investigating the neural mechanisms underlying mathematical cognition using advanced generative AI techniques applied to functional MRI (fMRI) data. Our approach emphasizes similarities over differences and promotes ethical AI practices in neuroimaging research.

🆕 NEW: Economic Bias Detection

Addressing a Critical Gap in Economic Research: Economic studies using neuroimaging data are particularly vulnerable to bias that can lead to incorrect policy decisions and perpetuate inequality. MCP-fMRI now includes comprehensive economic bias detection specifically designed for economics researchers.

Primary Goals

Investigate neural mechanisms underlying mathematical cognition in Japanese populations
Emphasize gender similarities rather than differences in mathematical abilities
🆕 Detect and mitigate economic bias in neuroimaging research applications
Apply generative AI to enhance fMRI analysis capabilities and address small dataset challenges
Integrate cultural context specific to Japanese sociocultural factors
Establish ethical guidelines for AI-based research in neuroimaging and economics

Research Philosophy

We follow evidence-based approaches showing that gender similarities dominate mathematical cognition at the neural level, with special attention to economic factors that can bias research and policy.

🚨 Economic Bias: A Critical Problem

Why Economic Bias Matters

Bias in economic research can:

Lead to incorrect policy decisions affecting millions
Perpetuate economic inequalities through biased research
Undermine scientific credibility in policy-relevant research
Justify discrimination in hiring, education, and investment

Types of Economic Bias MCP-fMRI Detects

🎯 Selection Bias: Non-representative samples by income/education
💰 SES Confounding: Socioeconomic status affects both brain and behavior
📅 Temporal Bias: Economic conditions during data collection
🤖 Algorithmic Bias: Models discriminating by economic factors
🏛️ Policy Misuse: Research used to justify economic discrimination

🚀 Installation

From PyPI (Recommended)

pip install mcp-fmri

Optional Dependencies

# For neuroimaging support
pip install mcp-fmri[neuroimaging]

# For dashboard functionality
pip install mcp-fmri[dash]

# For development
pip install mcp-fmri[dev]

🔧 Quick Economic Bias Check

Before conducting any economic analysis, run a quick bias check:

from mcp_fmri import quick_economic_bias_check
import pandas as pd

# Load your demographics data
demographics = pd.read_csv('your_demographics.csv')

# Quick bias check
bias_check = quick_economic_bias_check(demographics)

print(bias_check['summary'])
for warning in bias_check['warnings']:
    print(warning)
for rec in bias_check['recommendations']:
    print(f"📋 {rec}")

💡 Economic Bias Detection Examples

1. Comprehensive Economic Bias Analysis

from mcp_fmri.economic_bias import (
    EconomicBiasDetector,
    run_comprehensive_economic_bias_analysis
)

# Initialize detector for economics research
detector = EconomicBiasDetector(
    sensitivity='high',  # Strict bias detection
    economic_context='japanese'  # Cultural context
)

# Detect selection bias
selection_bias = detector.detect_selection_bias_economic(demographics)

if selection_bias['economic_selection_bias']:
    print("⚠️ Economic selection bias detected!")
    print(f"Bias severity: {selection_bias['bias_severity']:.2f}")
    print("Recommendation: Implement stratified sampling")

# Detect SES confounding
ses_confounding = detector.detect_ses_confounding(
    demographics, brain_data, performance_data
)

if ses_confounding['ses_brain_confounding']:
    print("⚠️ SES confounding detected!")
    print(f"SES-brain correlation: {ses_confounding['ses_brain_correlation']:.3f}")
    print("Recommendation: Control for SES in all analyses")

2. Economic Policy-Safe Analysis

from mcp_fmri import (
    GenderSimilarityAnalyzer,
    JapaneseCulturalContext
)
from mcp_fmri.economic_bias import EconomicBiasDetector

# Initialize with economic bias protection
analyzer = GenderSimilarityAnalyzer(
    ethical_guidelines=True,
    bias_detection=True,  # Includes economic bias detection
    cultural_context="japanese"
)

# Japanese cultural & economic context
context = JapaneseCulturalContext()
economic_detector = EconomicBiasDetector(economic_context='japanese')

# Load and validate data
data_dict = analyzer.load_preprocessed_data("your_data_path")

# Check for economic bias BEFORE analysis
economic_bias = run_comprehensive_economic_bias_analysis(
    data_dict, economic_context='japanese'
)

if economic_bias['overall_assessment']['bias_detected']:
    print(f"🚨 Economic bias detected: {economic_bias['overall_assessment']['bias_level']}")
    print("Applying bias mitigation before proceeding...")
    
    # Apply recommended mitigations
    for rec in economic_bias['comprehensive_recommendations']:
        print(f"📋 {rec}")
    
    # DO NOT proceed with policy recommendations until bias is addressed
    print("⚠️ Address bias before making policy recommendations")
else:
    # Safe to proceed with analysis
    similarities, bias_results = analyzer.analyze_similarities(data_dict)
    print(f"✅ Analysis complete - safe for policy considerations")

3. Economics-Specific Applications

# For economics researchers
from mcp_fmri.economic_bias import EconomicBiasDetector

# Assess workforce potential without discrimination
def assess_workforce_potential(population_data):
    detector = EconomicBiasDetector(sensitivity='high')
    
    # Check for bias in workforce assessment
    bias_flags = detector.detect_selection_bias_economic(
        population_data['demographics']
    )
    
    if bias_flags['economic_selection_bias']:
        return {
            'status': 'bias_detected',
            'recommendation': 'Expand sampling before workforce assessment',
            'bias_severity': bias_flags['bias_severity']
        }
    
    # Safe to proceed with individual-focused assessment
    individual_assessments = []
    for participant in population_data['participants']:
        # Focus on individual potential, not group predictions
        potential = assess_individual_potential(participant)
        individual_assessments.append(potential)
    
    return {
        'status': 'assessment_complete',
        'individual_assessments': individual_assessments,
        'avoid_group_generalizations': True
    }

# Educational resource allocation
def allocate_education_resources(school_data, budget):
    detector = EconomicBiasDetector(economic_context='japanese')
    
    allocation_results = {}
    for school_id, data in school_data.items():
        # Check for bias in needs assessment
        bias_check = detector.detect_selection_bias_economic(data['demographics'])
        
        if bias_check['economic_selection_bias']:
            print(f"⚠️ Bias detected in {school_id} needs assessment")
            # Apply bias correction before allocation
            needs = apply_bias_corrected_assessment(data)
        else:
            needs = assess_educational_needs(data)
        
        allocation_results[school_id] = calculate_fair_allocation(needs, budget)
    
    return allocation_results

📊 Key Features

🧠 Neural Analysis (Enhanced)

Similarity-focused fMRI preprocessing pipeline
Gender similarity detection models
🆕 Economic bias-aware analysis
Cultural context integration
Comprehensive bias detection and mitigation

💰 Economic Bias Detection (NEW)

Selection bias detection by income, education, employment
SES confounding analysis with brain and performance data
Temporal economic bias detection during study periods
Algorithmic bias detection in predictive models
Policy misuse prevention safeguards

🌏 Cultural Considerations

Japanese educational system context
🆕 Japanese economic context (collectivist values, employment patterns)
Collectivist culture framework
Stereotype acquisition patterns
Regional economic diversity analysis

🤖 Generative AI Components

Variational Autoencoders (VAE) for feature learning
🆕 Bias-aware data augmentation
Transfer learning across populations
Economic confound-controlled synthetic data generation

📊 Ethical Framework (Enhanced)

Similarity emphasis over differences
🆕 Economic discrimination prevention
Individual focus over group generalizations
Comprehensive bias mitigation techniques
🆕 Policy-safe reporting guidelines
Cultural sensitivity integration

📈 Economic Research Applications

1. Educational Policy 💎

Bias-free assessment of educational interventions
Fair resource allocation across schools
Individual-focused rather than group-based policies

2. Workforce Development 🏢

Ethical assessment of mathematical potential
Discrimination-free hiring tool development
Individual career pathway optimization

3. Economic Development 🌟

Regional capacity assessment with bias controls
Evidence-based investment recommendations
Cultural context-aware development planning

🛡️ Bias Prevention Safeguards

Automated Safeguards

# Automatic bias checking
from mcp_fmri import set_config

# Enable strict bias monitoring
set_config(
    ethical_guidelines=True,
    economic_bias_detection=True,
    auto_bias_check=True  # Automatically check for bias
)

# All analyses now include automatic bias detection
analyzer = GenderSimilarityAnalyzer()  # Automatically bias-aware

Policy Application Safeguards

from mcp_fmri.utils import validate_policy_application

# Before using research for policy
policy_validation = validate_policy_application(
    research_results=your_results,
    intended_use='educational_resource_allocation',
    population='japanese_students'
)

if not policy_validation['safe_for_policy']:
    print("🚨 UNSAFE for policy application")
    for issue in policy_validation['issues']:
        print(f"⚠️ {issue}")
else:
    print("✅ Safe for policy application with safeguards")

📖 Command Line Interface

# Preprocess with economic bias checking
mcp-fmri-preprocess --input /data/raw --output /data/processed \
                    --economic-bias-check --cultural-context japanese

# Analyze with comprehensive bias detection
mcp-fmri-analyze --data /data/processed --output /results \
                 --economic-bias-detection --policy-safe-reporting

🧪 Testing Your Data for Economic Bias

# Test your existing research for economic bias
from mcp_fmri.economic_bias import run_comprehensive_economic_bias_analysis

# Load your data
data_dict = {
    'demographics': your_demographics_df,
    'brain_data': your_brain_data,
    'performance_data': your_performance_scores,
    'study_timeline': your_study_dates
}

# Comprehensive bias analysis
bias_results = run_comprehensive_economic_bias_analysis(
    data_dict, 
    economic_context='japanese'
)

print(f"Overall bias level: {bias_results['overall_assessment']['bias_level']}")
print(f"Bias score: {bias_results['overall_assessment']['bias_score']:.3f}")

if bias_results['overall_assessment']['bias_detected']:
    print("\n🚨 BIAS DETECTED - Recommendations:")
    for rec in bias_results['comprehensive_recommendations']:
        print(f"📋 {rec}")
else:
    print("\n✅ No significant economic bias detected")

📚 Documentation

Economic Bias Guide - Comprehensive bias detection guide
Economic Implications - Policy and economic applications
Quick Start Guide - Get started in 5 minutes
Ethical Guidelines - Complete ethical framework
API Documentation - Detailed API reference

🔬 Scientific Background

Economic Bias Research Context

Recent studies highlight the critical importance of bias detection in economic research:

Economic inequality effects: SES significantly affects both neural development and mathematical performance, creating spurious correlations
Policy implications: Biased neuroimaging research has been used to justify discriminatory educational and hiring practices
Cultural factors: Economic systems and cultural values interact, requiring context-specific analysis
Methodological gaps: Most neuroimaging studies lack comprehensive economic bias detection

Original Research Foundation

Gender similarities hypothesis: Large-scale studies show more similarities than differences in mathematical cognition (Hyde et al., 2008)
Neural similarity findings: fMRI studies in children (3-10 years) show no significant gender differences in mathematical brain activation (Kersey et al., 2019)
Cultural factors: Japanese children may acquire gender stereotypes later than Western populations, suggesting environmental influences (Tatsuno et al., 2022)

⚠️ Critical Warnings for Economics Researchers

🚨 NEVER Use For Discrimination

# ❌ WRONG - Group-based decisions
if participant['gender'] == 'female':
    predicted_math_ability = model.predict_low()
    
# ✅ CORRECT - Individual assessment
potential = assess_individual_potential(
    participant['brain_data'],
    control_for_bias=True,
    cultural_context='japanese'
)

🛡️ Always Check Bias First

# ❌ WRONG - Direct analysis without bias check
results = analyze_without_bias_check(data)
make_policy_recommendations(results)  # DANGEROUS!

# ✅ CORRECT - Bias-aware workflow
bias_check = quick_economic_bias_check(data)
if bias_check['warnings']:
    print("Address bias before proceeding")
    apply_bias_mitigation(data)

results = analyze_with_bias_controls(data)
make_responsible_recommendations(results)

📊 Focus on Individual Differences

# ❌ WRONG - Group-level generalizations
print(f"Group X performs {mean_diff:.2f} points lower")

# ✅ CORRECT - Individual variation emphasis
print(f"Individual differences ({individual_std:.2f}) exceed")
print(f"group differences ({group_diff:.2f}) by {ratio:.1f}:1")
print("Focus on individual assessment and support")

🤝 Contributing

We welcome contributions that align with our ethical research principles, especially:

🔍 Economic bias detection improvements
🌍 Additional cultural contexts
📊 Policy-safe analysis methods
🛡️ Bias mitigation techniques
📖 Documentation and examples

See CONTRIBUTING.md for detailed guidelines.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

Economics research community for highlighting bias concerns
Neuroimaging community for open science practices
Ethical AI researchers for bias mitigation frameworks
Japanese research institutions for cultural context insights
Policy researchers emphasizing responsible research translation

📞 Support

Documentation: https://tatsuru-kikuchi.github.io/MCP-fMRI/
Issues: GitHub Issues
Discussions: GitHub Discussions
Economic Research Questions: Tag issues with economics or bias-detection

🎯 Quick Start for Economics Researchers

# Install
pip install mcp-fmri

# Quick bias check
from mcp_fmri import quick_economic_bias_check
bias_result = quick_economic_bias_check(your_demographics)
print(bias_result['summary'])

# If bias detected, see docs/bias_reduction_economics.md
# If no bias, proceed with analysis
from mcp_fmri import GenderSimilarityAnalyzer
analyzer = GenderSimilarityAnalyzer(ethical_guidelines=True)
results = analyzer.analyze_similarities(your_data)

# Generate policy-safe report
report = analyzer.generate_report()
print(report)

⚠️ Important Note: This research emphasizes gender similarities in mathematical cognition and includes comprehensive economic bias detection. Results should never be used to justify discrimination or reinforce stereotypes. All findings should be interpreted within their cultural and economic context with emphasis on individual differences over group generalizations.

🏛️ Policy Note: Before using any findings for policy decisions, ensure comprehensive bias analysis has been conducted and appropriate safeguards are in place to prevent discriminatory applications.