> ## Documentation Index
> Fetch the complete documentation index at: https://docs.enkryptai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Bias Detector

This detector identifies and mitigates potential biases in the generated text. It helps ensure that the content is fair and unbiased across various dimensions such as gender, race, or age.

## Example request:

<CodeGroup>
  ```python Python theme={"system"}
  import requests
  import json
  import os

  url = "https://api.enkryptai.com/guardrails/detect"

  payload = json.dumps({
      "text": "All programmers are men and women can't code.",
      "detectors": {
          "bias": {
              "enabled": True
          }
      }
  })

  headers = {
      'Content-Type': 'application/json',
      'apikey': os.getenv('ENKRYPTAI_API_KEY')
  }

  response = requests.request("POST", url, headers=headers, data=payload)

  formatted_response = json.dumps(json.loads(response.text), indent=4)
  print(formatted_response)
  ```

  ```python Python SDK theme={"system"}
  import os
  from enkryptai_sdk import *
  from dotenv import load_dotenv

  load_dotenv()

  ENKRYPT_API_KEY = os.getenv("ENKRYPTAI_API_KEY")
  ENKRYPT_BASE_URL = os.getenv("ENKRYPTAI_BASE_URL") or "https://api.enkryptai.com"

  guardrails_client = GuardrailsClient(api_key=ENKRYPT_API_KEY, base_url=ENKRYPT_BASE_URL)

  guardrails_config = GuardrailsConfig.bias()

  prompt = "All programmers are men and women can't code."

  detect_response = guardrails_client.detect(text=prompt, guardrails_config=guardrails_config)

  print(detect_response)

  # Print as a dictionary
  print(detect_response.to_dict())
  ```
</CodeGroup>

## Example response:

```json JSON theme={"system"}
{
  "summary": {
    "bias": 1
  },
  "details": {
    "bias": {
      "bias_detected": true,
      "debiased_text": "People of all genders can be programmers, with skills in coding not limited to any specific gender.",
      "compliance_mapping": {
        "owasp_llm_2025": [
          "LLM09:2025 Misinformation",
          "LLM04:2025 Data and Model Poisoning"
        ],
        "mitre_atlas": [],
        "nist_ai_rmf": [
          "MEASURE 2.1-2.5 (AI system bias evaluation & management)"
        ],
        "eu_ai_act": [
          "Article 10(2)(f), Article 15(3) (Bias detection, correction & mitigation)"
        ],
        "iso_iec_standards": [
          "ISO/IEC TR 24027: 5.2 (Bias in AI systems)"
        ]
      }
    }
  }
}
```