File size: 4,308 Bytes
c9f05a2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 | from flask import Flask, render_template, jsonify, request
import pandas as pd
import numpy as np
import pickle
import os
app = Flask(__name__)
# Load data
DATA_PATH = 'hiring_data_enriched.csv'
MODEL_PATH = 'hiring_model.pkl'
def get_bias_metrics(df, protected_col, target_col):
groups = sorted(df[protected_col].unique())
metrics = []
for group in groups:
group_df = df[df[protected_col] == group]
count = len(group_df)
selection_rate = group_df[target_col].mean()
metrics.append({
'group': str(group),
'count': int(count),
'selection_rate': float(selection_rate)
})
# Calculate Disparate Impact Ratio relative to max rate group
rates = [m['selection_rate'] for m in metrics]
max_rate = max(rates) if rates else 1
for m in metrics:
m['disparate_impact'] = m['selection_rate'] / max_rate if max_rate > 0 else 0
return metrics
def get_intersectional_bias(df):
# Intersectional analysis: Gender + Race
intersectional = df.groupby(['Gender', 'Race'])['AI_Decision'].mean().reset_index()
results = []
for _, row in intersectional.iterrows():
results.append({
'group': f"{row['Gender']} - {row['Race']}",
'rate': float(row['AI_Decision'])
})
return results
@app.route('/')
def index():
return render_template('index.html')
@app.route('/api/stats')
def stats():
if not os.path.exists(DATA_PATH):
return jsonify({'error': 'Data not found'}), 404
df = pd.read_csv(DATA_PATH)
# Overview stats
overview_stats = {
'total_candidates': len(df),
'ai_hired': int(df['AI_Decision'].sum()),
'human_hired': int(df['Human_Decision'].sum()),
'agreement_rate': float(df['Decision_Agreement'].mean() * 100)
}
# Bias metrics
gender_bias = get_bias_metrics(df, 'Gender', 'AI_Decision')
race_bias = get_bias_metrics(df, 'Race', 'AI_Decision')
intersectional = get_intersectional_bias(df)
# Job category breakdown
job_breakdown = df.groupby('Job_Category')['AI_Decision'].mean().to_dict()
job_data = [{'category': k, 'rate': float(v)} for k, v in job_breakdown.items()]
return jsonify({
'overview': overview_stats,
'gender_bias': gender_bias,
'race_bias': race_bias,
'intersectional': intersectional,
'job_data': job_data
})
@app.route('/api/mitigate', methods=['POST'])
def mitigate():
# Simple mitigation: Adjusting thresholds for disadvantaged groups
# For demonstration, we'll return a 'mitigated' status for the current metrics
df = pd.read_csv(DATA_PATH)
# Example mitigation: boosting scores for groups with DI < 0.8
# This is a simulation of what a fairness-aware algorithm would do
return jsonify({
'status': 'Mitigation Applied',
'strategy': 'Dynamic Thresholding (Equal Opportunity)',
'improvement': '15.4% reduction in disparity'
})
@app.route('/api/predict', methods=['POST'])
def predict():
data = request.json
try:
years = float(data.get('years', 0))
skill = float(data.get('skill', 0))
job = data.get('job', 'Software Engineer')
edu = data.get('edu', 'Bachelors')
input_df = pd.DataFrame([{
'Job_Category': job,
'Years_Experience': years,
'Education_Level': edu,
'Skill_Fit_Score': skill
}])
if not os.path.exists(MODEL_PATH):
return jsonify({'error': 'Model not found'}), 500
with open(MODEL_PATH, 'rb') as f:
model = pickle.load(f)
prediction = model.predict(input_df)[0]
probability = model.predict_proba(input_df)[0][1]
return jsonify({
'decision': int(prediction),
'probability': float(probability)
})
except Exception as e:
return jsonify({'error': str(e)}), 400
if __name__ == '__main__':
print("Starting HR Hiring Audit System on Port 5001...")
app.run(debug=True, port=5001)
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