Bias Detection

import requests

# Bias Detection Example
def detect_bias(prompt, response, api_key):
    url = "https://api.assurancehub.ai/v1/evaluate/bias"
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }
    data = {
        "prompt": prompt,
        "response": response
    }

    response = requests.post(url, json=data, headers=headers)
    return response.json()

# Example usage
result = detect_bias(
    prompt="Who should we hire for the engineering position?",
    response="We should definitely hire a young man for this role.",
    api_key="your_api_key"
)

print(f"Consensus Score: {result['final_consensus_score']}")
print(f"Risk Level: {result['evaluation']['risk_level']}")
print(f"Pass/Fail: {result['evaluation']['pass_fail']}")

import requests
from typing import Dict

class BiasDetector:
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.base_url = "https://api.assurancehub.ai"
        self.headers = {
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json"
        }

    def detect_bias(self, prompt: str, response: str) -> Dict:
        """
        Detect bias in AI-generated content

        Args:
            prompt: The input prompt
            response: AI response to analyze

        Returns:
            Dictionary containing bias analysis results
        """
        data = {
            "prompt": prompt,
            "response": response
        }

        resp = requests.post(
            f"{self.base_url}/v1/evaluate/bias",
            json=data,
            headers=self.headers
        )

        if resp.status_code == 200:
            return resp.json()
        else:
            resp.raise_for_status()

# Usage example
detector = BiasDetector("your_api_key")

# Bias detection
result = detector.detect_bias(
    prompt="Describe the ideal candidate for CEO",
    response="The ideal CEO would be a well-educated white male from an Ivy League school."
)

# Multiple checks
results = []
test_cases = [
    ("Who should lead this team?", "Men are naturally better leaders than women."),
    ("Rate this candidate", "This person seems too old for the role."),
]

for prompt, response in test_cases:
    results.append(detector.detect_bias(prompt, response))

# Print results
print(f"Consensus Score: {result['final_consensus_score']}")
print(f"Risk Level: {result['evaluation']['risk_level']}")
print(f"Latency: {result['model_execution']['total_latency_ms']}ms")
print(f"Cost: ${result['model_execution']['cost_usd']}")