Skewed Distributions in Data Science

N. Dasgupta
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引用次数: 2

Abstract

This column is about raising questions, rather than providing answers. These days “data based decision making” is the rage among administrators in both industry and academia. The desire for this dependence on algorithms stems from the general idea that “humans are biased but machines are not”. More and more social decisions, like qualifying for welfare are, are made using algorithms. With this, the data scientists, who are behind the algorithms, are given a lot of power (and responsibility.) In this column, I discuss demographic characteristics of data scientists with conjectures on why this group is non-diverse. Should we allow a small group of non-representative people to make decisions that affect affect larger society?
数据科学中的偏斜分布
本专栏旨在提出问题,而不是提供答案。如今,“基于数据的决策”在工业界和学术界的管理人员中都很流行。这种对算法的依赖源于“人类有偏见,但机器没有”的普遍观点。越来越多的社会决策,比如获得福利的资格,都是用算法做出的。这样,算法背后的数据科学家就被赋予了很大的权力(和责任)。在本专栏中,我将讨论数据科学家的人口统计学特征,并推测为什么这个群体没有多样性。我们应该允许一小群没有代表性的人做出影响整个社会的决定吗?
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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