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### Bias and Fairness in Automated Decision-Making
AI systems trained on biased or incomplete data risk false positives—incorrectly flagging legitimate behavior as malicious—which can disrupt services, damage reputations, and marginalize users. Ensuring algorithmic fairness is paramount, particularly in high-stakes environments like finance and healthcare.
The black box nature of many AI models complicates trust and accountability. Security teams must understand how decisions are made to validate actions, comply with regulations like GDPR, and build stakeholder confidence. Explainable AI (XAI) is emerging as a critical framework to demystify these systems.

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