你看到的就是你得到的？表征标准对招聘中人的偏见的影响
What You See Is What You Get? The Impact of Representation Criteria on Human Bias in Hiring
摘要：
虽然决策中的系统性偏见被广泛记录，但人们对它们如何从不同来源出现的了解较少。我们提出了一个受控的实验平台，通过将世界分布（特定职业中候选人的性别细分）的影响与人类决策中的偏见脱钩，来研究招聘中的性别偏见。我们探讨了\textit{representation criteria}的有效性，固定比例显示的候选人，作为一种干预策略，通过进行实验测量人类决策者的排名，他们会推荐谁作为潜在的雇员，以减轻性别偏见。在不同性别比例的职业中进行的实验表明，平衡候选人名单中的性别代表性可以纠正某些职业的偏见，这些职业的世界分布是倾斜的，尽管这样做对人类持续偏好起作用的其他职业没有影响。我们发现，决策者的性别，决策任务的复杂性和候选人名单中性别的代表性过高和过低都会影响最终的决定。通过解耦偏差源，我们可以更好地隔离人在环系统中的偏差缓解策略。
英文摘要：
Although systematic biases in decision-making are widely documented, the ways in which they emerge from different sources is less understood. We present a controlled experimental platform to study gender bias in hiring by decoupling the effect of world distribution (the gender breakdown of candidates in a specific profession) from bias in human decision-making. We explore the effectiveness of \textit{representation criteria}, fixed proportional display of candidates, as an intervention strategy for mitigation of gender bias by conducting experiments measuring human decision-makers' rankings for who they would recommend as potential hires. Experiments across professions with varying gender proportions show that balancing gender representation in candidate slates can correct biases for some professions where the world distribution is skewed, although doing so has no impact on other professions where human persistent preferences are at play. We show that the gender of the decision-maker, complexity of the decision-making task and over- and under-representation of genders in the candidate slate can all impact the final decision. By decoupling sources of bias, we can better isolate strategies for bias mitigation in human-in-the-loop systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
