英文标题：
《A Short-term Intervention for Long-term Fairness in the Labor Market》
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作者：
Lily Hu and Yiling Chen
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最新提交年份：
2018
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英文摘要：
  The persistence of racial inequality in the U.S. labor market against a general backdrop of formal equality of opportunity is a troubling phenomenon that has significant ramifications on the design of hiring policies. In this paper, we show that current group disparate outcomes may be immovable even when hiring decisions are bound by an input-output notion of \"individual fairness.\" Instead, we construct a dynamic reputational model of the labor market that illustrates the reinforcing nature of asymmetric outcomes resulting from groups\' divergent accesses to resources and as a result, investment choices. To address these disparities, we adopt a dual labor market composed of a Temporary Labor Market (TLM), in which firms\' hiring strategies are constrained to ensure statistical parity of workers granted entry into the pipeline, and a Permanent Labor Market (PLM), in which firms hire top performers as desired. Individual worker reputations produce externalities for their group; the corresponding feedback loop raises the collective reputation of the initially disadvantaged group via a TLM fairness intervention that need not be permanent. We show that such a restriction on hiring practices induces an equilibrium that, under particular market conditions, Pareto-dominates those arising from strategies that statistically discriminate or employ a \"group-blind\" criterion. The enduring nature of equilibria that are both inequitable and Pareto suboptimal suggests that fairness interventions beyond procedural checks of hiring decisions will be of critical importance in a world where machines play a greater role in the employment process.
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中文摘要：
在正式机会平等的大背景下，美国劳动力市场持续存在种族不平等，这是一个令人不安的现象，对招聘政策的设计产生了重大影响。在本文中，我们表明，即使雇佣决策受到“个人公平”的投入产出概念的约束，当前的群体差异结果也可能是不可改变的相反，我们构建了劳动力市场的动态声誉模型，该模型说明了群体对资源的不同获取以及投资选择所导致的不对称结果的强化性质。为了解决这些差异，我们采用了由临时劳动力市场（TLM）和永久劳动力市场（PLM）组成的双重劳动力市场，前者限制公司的雇佣策略，以确保获准进入管道的工人的统计均等，后者则根据需要雇佣表现优异的员工。工人个人声誉为其群体产生外部效应；相应的反馈回路通过TLM公平干预提高了最初处境不利群体的集体声誉，这种干预不需要是永久性的。我们表明，这种对雇佣行为的限制导致了一种均衡，即在特定的市场条件下，帕累托支配着那些在统计上歧视或采用“群体盲”标准的策略所产生的均衡。不公平和帕累托次优均衡的持久性表明，在一个机器在就业过程中发挥更大作用的世界里，招聘决策程序检查之外的公平干预将至关重要。
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Science and Game Theory        计算机科学与博弈论
分类描述：Covers all theoretical and applied aspects at the intersection of computer science and game theory, including work in mechanism design, learning in games (which may overlap with Learning), foundations of agent modeling in games (which may overlap with Multiagent systems), coordination, specification and formal methods for non-cooperative computational environments. The area also deals with applications of game theory to areas such as electronic commerce.
涵盖计算机科学和博弈论交叉的所有理论和应用方面，包括机制设计的工作，游戏中的学习（可能与学习重叠），游戏中的agent建模的基础（可能与多agent系统重叠），非合作计算环境的协调、规范和形式化方法。该领域还涉及博弈论在电子商务等领域的应用。
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一级分类：Quantitative Finance        数量金融学
二级分类：General Finance        一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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