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2026年9月，麻省理工学院研究人员分析多家机构采用同一种招聘算法可能产生的影响，认为不能只凭算法是否统一，就判断结果一定更好或更坏。研究通过模型和模拟比较不同情境，指出算法准确率、信息流动方式以及候选人的选择机会，都会改变最终效果。

一个受到关注的问题是，多家企业依赖相同评分，可能不断挑选具有类似特征的人，从而减少发现其他优秀候选人的机会。研究认为，这种信息同质化比简单地讨论是否使用同一套工具更值得重视。在部分模拟中，把多个算法的判断合并为集成评分，可以保留不同观点的信息，并取得与分别使用多套算法相当甚至更好的效果。适当引入探索机制也是可能的改善方向。不过，这些结论来自特定假设，并不表示任何统一招聘平台都公平或有效。团队希望进一步用实际数据检验不同设计，帮助机构在使用人工智能时兼顾预测准确度与发现新机会的能力。

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