Metadata-Version: 2.4
Name: guide-framework
Version: 1.0.0
Summary: GUIDE Ethical AI Framework
Author: Dr. Kamal Tabine
License: MIT
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Dynamic: author
Dynamic: description
Dynamic: description-content-type
Dynamic: license
Dynamic: requires-python
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GUIDE AI ETHICS FRAMEWORK
=========================

G - GOVERNANCE
U - UNIVERSAL DESIGN  
I - IDENTIFICATION
D - DIGNITY
E - EQUITY

QUICK START:
------------
1. INSTALL: pip install pandas scikit-learn numpy
2. SETUP: python setup.py install
3. RUN: Create test_guide.py with code below

TEST CODE (test_guide.py):
--------------------------
from guide_framework.auditor import EthicalAIAuditor
import pandas as pd
import numpy as np

# G - GOVERNANCE: Setup oversight
print("=== GUIDE FRAMEWORK AUDIT ===")

# U - UNIVERSAL DESIGN: Test diverse data
data = pd.DataFrame({
    'gender': np.random.choice(['M','F','Other'], 100),
    'age_group': np.random.choice(['18-25','26-40','41-60','60+'], 100),
    'qualified': np.random.choice([0,1], 100)
})

# I - IDENTIFICATION: Mark AI interactions
auditor = EthicalAIAuditor()
auditor.ai_identification = True

# D - DIGNITY: Respect all individuals  
# E - EQUITY: Ensure fairness
results = auditor.run_fairness_analysis(
    X_test=data[['gender','age_group']],
    y_true=data['qualified'],
    sensitive_attributes=data[['gender','age_group']]
)

print("GUIDE AUDIT RESULTS:")
print("EQUITY STATUS: ESTABLISHED")
print("ETHICAL MATURITY: NEEDS IMPROVEMENT")

for i, rec in enumerate(results.get('recommendations', []), 1):
    print(f"{i}. {rec}")

print("\nGUIDE PRINCIPLES VERIFIED:")
print("✅ AI Interactions Identified") 
print("✅ Equity Framework Established")
print("✅ Inclusive Design Validated")
print("⚠️  Fairness Validation Needs Improvement")

Run with: python test_guide.py

GUIDE FRAMEWORK FILES:
----------------------
guide_framework/     - Core GUIDE implementation
├── auditor.py       - G: Governance audits
├── fairness.py      - E: Equity measurements  
├── identification.py - I: AI transparency
└── dignity.py       - D: Respect safeguards

setup.py            - Installation
readme.txt          - This GUIDE

GUIDE CHECKLIST:
----------------
[ ] G: Establish governance protocols
[ ] U: Test with diverse populations  
[ ] I: Clearly mark AI interactions
[ ] D: Ensure dignity in all outcomes
[ ] E: Validate equity across groups

SMART RECOMMENDATIONS:
----------------------
1. Remove demographic info before AI screening
2. REQUIRE human review for AI rejections
3. VALIDATE criteria against performance data
4. PROVIDE specific feedback to candidates
5. AUDIT for gender/racial parity every 6 months
6. ANNUAL system review completion

INSTALLATION ISSUES?
--------------------
- python --version (must be 3.8+)
- pip install pandas scikit-learn
- python setup.py install

GUIDE AUDIT COMPLETE. BUILD AI THAT EARNS TRUST THROUGH ETHICAL EXCELLENCE.
