Psychometric Methods to Evaluate Measurement and Algorithmic Bias in Automated Scoring

IF 1.4 4区 心理学 Q3 PSYCHOLOGY, APPLIED
Matthew S. Johnson, Xiang Liu, Daniel F. McCaffrey
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引用次数: 4

Abstract

With the increasing use of automated scores in operational testing settings comes the need to understand the ways in which they can yield biased and unfair results. In this paper, we provide a brief survey of some of the ways in which the predictive methods used in automated scoring can lead to biased, and thus unfair automated scores. After providing definitions of fairness from machine learning and a psychometric framework to study them, we demonstrate how modeling decisions, like omitting variables, using proxy measures or confounded variables, and even the optimization criterion in estimation can lead to biased and unfair automated scores. We then introduce two simple methods for evaluating bias, evaluate their statistical properties through simulation, and apply to an item from a large-scale reading assessment.

评估自动评分测量和算法偏差的心理测量学方法
随着在操作测试设置中越来越多地使用自动化分数,需要了解它们可能产生偏见和不公平结果的方式。在本文中,我们简要介绍了自动评分中使用的预测方法可能导致有偏见,从而导致不公平的自动评分的一些方式。在提供了机器学习公平性的定义和研究它们的心理测量框架之后,我们展示了建模决策,如省略变量,使用代理度量或混淆变量,甚至是估计中的优化标准,如何导致有偏见和不公平的自动分数。然后,我们介绍了两种评估偏差的简单方法,通过模拟评估它们的统计特性,并将其应用于大规模阅读评估中的一个项目。
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来源期刊
CiteScore
2.30
自引率
7.70%
发文量
46
期刊介绍: The Journal of Educational Measurement (JEM) publishes original measurement research, provides reviews of measurement publications, and reports on innovative measurement applications. The topics addressed will interest those concerned with the practice of measurement in field settings, as well as be of interest to measurement theorists. In addition to presenting new contributions to measurement theory and practice, JEM also serves as a vehicle for improving educational measurement applications in a variety of settings.
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