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Addressing Bias In Algorithmic Hiring Tools
- AI hiring systems can introduce bias if the training data is biased.
- Examples include gender bias, racial bias, and affinity bias.
- To combat bias, companies can diversify training data, implement anonymized resume screening, conduct regular algorithm audits, and use hybrid hiring processes.
- Failing to address bias can lead to legal and reputational risks, hinder diversity, and limit innovation.
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