Information Retrieval (IR) systems are designed to deliver relevant content,
but traditional systems may not optimize rankings for fairness, neutrality, or
the balance of ideas. Consequently, IR can often introduce indexical biases, or
biases in the positional order of documents. Although indexical bias can
demonstrably affect people's opinion, voting patterns, and other behaviors,
these issues remain understudied as the field lacks reliable metrics and
procedures for automatically measuring indexical bias. Towards this end, we
introduce the PAIR framework, which supports automatic bias audits for ranked
documents or entire IR systems. After introducing DUO, the first
general-purpose automatic bias metric, we run an extensive evaluation of 8 IR
systems on a new corpus of 32k synthetic and 4.7k natural documents, with 4k
queries spanning 1.4k controversial issue topics. A human behavioral study
validates our approach, showing that our bias metric can help predict when and
how indexical bias will shift a reader's opinion.

信息检索系统的相关性排序可能引入指示偏见，该研究引入了自动偏见审核框架（PAIR framework）和偏见度量（bias metric），通过对 8 个信息检索系统进行评估，在一系列有争议的主题中，验证了指示偏见如何影响读者意见的预测。

信息检索中的索引偏见的测量和应对

Measuring and Addressing Indexical Bias in Information Retrieval

The use of automated decision tools in recruitment has received an increasing
amount of attention. In November 2021, the New York City Council passed a
legislation (Local Law 144) that mandates bias audits of Automated Employment
Decision Tools. From 15th April 2023, companies that use automated tools for
hiring or promoting employees are required to have these systems audited by an
independent entity. Auditors are asked to compute bias metrics that compare
outcomes for different groups, based on sex/gender and race/ethnicity
categories at a minimum. Local Law 144 proposes novel bias metrics for
regression tasks (scenarios where the automated system scores candidates with a
continuous range of values). A previous version of the legislation proposed a
bias metric that compared the mean scores of different groups. The new revised
bias metric compares the proportion of candidates in each group that falls
above the median. In this paper, we argue that both metrics fail to capture
distributional differences over the whole domain, and therefore cannot reliably
detect bias. We first introduce two metrics, as possible alternatives to the
legislation metrics. We then compare these metrics over a range of theoretical
examples, for which the legislation proposed metrics seem to underestimate
bias. Finally, we study real data and show that the legislation metrics can
similarly fail in a real-world recruitment application.

本文讨论了 Local Law 144 对于自动决策工具中的偏见检测指标不足的问题，并提出了可能的替代指标。通过对一些理论例子和实际数据的对比，我们发现新的指标更能准确地检测出偏见。