
TL;DR
This paper introduces measurement modeling from social sciences as a framework to better understand and address fairness issues in computational systems, emphasizing the importance of aligning theoretical constructs with their operationalizations.
Contribution
It applies measurement modeling to fairness, offering new tools for explicit assumption testing and clarifying debates about fairness definitions in computational systems.
Findings
Measurement modeling reveals potential mismatches causing fairness harms.
Explicit testing of assumptions can mitigate fairness-related issues.
Clarifies the theoretical underpinnings of fairness debates.
Abstract
We propose measurement modeling from the quantitative social sciences as a framework for understanding fairness in computational systems. Computational systems often involve unobservable theoretical constructs, such as socioeconomic status, teacher effectiveness, and risk of recidivism. Such constructs cannot be measured directly and must instead be inferred from measurements of observable properties (and other unobservable theoretical constructs) thought to be related to them -- i.e., operationalized via a measurement model. This process, which necessarily involves making assumptions, introduces the potential for mismatches between the theoretical understanding of the construct purported to be measured and its operationalization. We argue that many of the harms discussed in the literature on fairness in computational systems are direct results of such mismatches. We show how some of…
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