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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.

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Example request:

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)

​
Example response:

JSON
{
  "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)"
        ]
      }
    }
  }
}