论文标题
为在线市场选择算法公平度量指标：检测和量化LinkedIn上的算法偏差
Choosing an algorithmic fairness metric for an online marketplace: Detecting and quantifying algorithmic bias on LinkedIn
论文作者
论文摘要
在本文中，我们得出了一个算法公平度量指标，从同样合格的候选人的公平概念中，用于两边市场常用的建议算法。我们从经济文献中借鉴了有关歧视的经济文献，以进行检测仅归因于算法的偏见，而不是平台使用者的其他来源，例如社会不平等或人类偏见。我们使用所提出的方法来测量和量化算法偏差相对于求职者和雇主使用的流行在线平台LinkedIn使用的两种算法的性别。此外，我们引入了一个框架和理由，以区分算法偏见和人类偏见，这两种偏见都可以在算法向人类用户提出建议的双面平台上存在。最后，我们讨论了其他一些常见的算法公平指标的缺点，以及为什么他们没有为同样合格的候选人抓住机会平等机会的公平概念。
In this paper, we derive an algorithmic fairness metric from the fairness notion of equal opportunity for equally qualified candidates for recommendation algorithms commonly used by two-sided marketplaces. We borrow from the economic literature on discrimination to arrive at a test for detecting bias that is solely attributable to the algorithm, as opposed to other sources such as societal inequality or human bias on the part of platform users. We use the proposed method to measure and quantify algorithmic bias with respect to gender of two algorithms used by LinkedIn, a popular online platform used by job seekers and employers. Moreover, we introduce a framework and the rationale for distinguishing algorithmic bias from human bias, both of which can potentially exist on a two-sided platform where algorithms make recommendations to human users. Finally, we discuss the shortcomings of a few other common algorithmic fairness metrics and why they do not capture the fairness notion of equal opportunity for equally qualified candidates.
