STEM与日常科技·英语30篇（1）
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Algorithmic Bias: When Code Reflects Human Prejudice
算法偏见：当代码映射人类偏见
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Algorithms learn patterns from historical data, which may contain social biases or inequalities.
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If past hiring records favor one group, an AI recruiter might repeat that pattern unknowingly.
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Bias can appear in facial recognition, loan approvals, or even medical diagnosis tools.
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Researchers test models using diverse datasets to uncover unfair performance gaps.
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Transparency alone isn’t enough—some AI systems are too complex to interpret fully.
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Regulators now ask developers to document training data sources and fairness metrics.
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Community input helps identify blind spots that engineers might overlook.
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Fixing bias requires both technical audits and inclusive design teams.
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Ethical AI frameworks emphasize accountability, not just accuracy or speed.
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Ongoing monitoring is needed because real-world use can reveal hidden flaws.