Ranking, recommendation, and retrieval systems are widely used in online
platforms and other societal systems, including e-commerce, media-streaming,
admissions, gig platforms, and hiring. In the recent past, a large "fair
ranking" research literature has been developed around making these systems
fair to the individuals, providers, or content that are being ranked. Most of
this literature defines fairness for a single instance of retrieval, or as a
simple additive notion for multiple instances of retrievals over time. This
work provides a critical overview of this literature, detailing the often
context-specific concerns that such an approach misses: the gap between high
ranking placements and true provider utility, spillovers and compounding
effects over time, induced strategic incentives, and the effect of statistical
uncertainty. We then provide a path forward for a more holistic and
impact-oriented fair ranking research agenda, including methodological lessons
from other fields and the role of the broader stakeholder community in
overcoming data bottlenecks and designing effective regulatory environments.

对在线平台中对个人、提供方或内容进行公正排序的 “公正排名” 研究文献进行了批判性概述，提出了更全面和以影响为导向的公正排名研究议程的前景，包括其他领域的方法论教训以及更广泛利益相关者社区在克服数据瓶颈和设计有效监管环境方面的作用。