{
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "What is AI bias in hiring?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "AI bias in hiring occurs when recruitment algorithms produce unfair outcomes for certain groups of candidates. This usually happens when AI models are trained on historical hiring data that contains existing biases, which the system may unintentionally learn and repeat during candidate evaluation."
      }
    },
    {
      "@type": "Question",
      "name": "Can AI recruitment tools reduce hiring bias?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Yes, AI recruitment tools can help reduce some forms of bias by standardizing candidate evaluation and focusing on skills and qualifications. Responsible implementation, diverse training data, and human oversight are essential to ensure fair hiring outcomes."
      }
    },
    {
      "@type": "Question",
      "name": "Why do AI hiring systems sometimes become biased?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "AI hiring systems can become biased if they are trained on historical data that reflects past hiring inequalities. Bias can also arise from poor algorithm design, limited datasets, or a lack of transparency in how candidate evaluations are performed."
      }
    },
    {
      "@type": "Question",
      "name": "Are AI-powered recruitment platforms designed to reduce bias?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Many modern recruitment platforms aim to reduce bias by combining AI automation with structured hiring processes and human oversight. Solutions such as iSmartRecruit use AI mainly to assist recruiters rather than automate final hiring decisions."
      }
    },
    {
      "@type": "Question",
      "name": "What should recruitment leaders look for in AI hiring tools?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Recruitment leaders should evaluate AI hiring tools based on transparency, fairness, and human oversight. It is important to understand how candidate evaluations are made and whether the platform allows monitoring to detect potential bias."
      }
    }
  ]
}