Representativeness is a foundational yet slippery concept. Though familiar at first blush, it lacks a single precise meaning. Instead, meanings range from typical or characteristic, to a proportionate match between sample and population, to a more general sense of accuracy, generalizability, coverage, or inclusiveness. Moreover, the concept has long been contested. In statistics, debates about the merits and methods of selecting a representative sample date back to the late 19th century; in politics, debates about the value of likeness as a logic of political representation are older still. Today, as the concept crops up in the study of fairness and accountability in machine learning, we need to carefully consider the term's meanings in order to communicate clearly and account for their normative implications. In this paper, we ask what representativeness means, how it is mobilized socially, and what values and ideals it communicates or confronts. We trace the concept's history in statistics and discuss normative tensions concerning its relationship to likeness, exclusion, authority, and aspiration. We draw on these analyses to think through how representativeness is used in FAccT debates, with emphasis on data, shift, participation, and power.

翻译：代表性是一个基础但很滑的概念。 虽然在最初的淡淡时熟悉,但它缺乏一个单一的准确含义。 相反,含义从典型或特征到抽样和人口之间的比例匹配,到更普遍的准确性、可概括性、涵盖性或包容性感不等。此外,这一概念长期以来一直受到质疑。在统计方面,关于选择代表性样本的优点和方法的辩论可追溯到19世纪末;在政治方面,关于相似性作为政治代表性逻辑的价值的辩论仍比以往更早。今天,当概念在机器学习中的公平和问责研究中出现时,我们需要仔细考虑该术语的含义,以便明确沟通和说明其规范影响。在本文中,我们询问何为代表性意味着什么,如何动员,以及它如何传播或面对什么价值观和理想。我们在统计中追溯该概念的历史,并讨论与其相似性、排斥、权威和愿望的关系有关的规范紧张。我们借助这些分析思考如何在FACCT辩论中使用代表性,重点是数据、变化、参与和权力。