STEM与日常科技·英语30篇（1）

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算法偏见：当代码映射人类偏见

Algorithms learn patterns from historical data, which may contain social biases or inequalities.
If past hiring records favor one group, an AI recruiter might repeat that pattern unknowingly.
Bias can appear in facial recognition, loan approvals, or even medical diagnosis tools.
Researchers test models using diverse datasets to uncover unfair performance gaps.
Transparency alone isn’t enough—some AI systems are too complex to interpret fully.
Regulators now ask developers to document training data sources and fairness metrics.
Community input helps identify blind spots that engineers might overlook.
Fixing bias requires both technical audits and inclusive design teams.
Ethical AI frameworks emphasize accountability, not just accuracy or speed.
Ongoing monitoring is needed because real-world use can reveal hidden flaws.