AI and the Social Sciences: Why all variables are not created equal

Res Publica 1:1-17 (2022)
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Abstract

This article argues that it is far from trivial to convert social science concepts into accurate categories on which algorithms work best. The literature raises this concern in a general way; for example, Deeks notes that legal concepts, such as proportionality, cannot be easily converted into code noting that ‘The meaning and application of these concepts is hotly debated, even among lawyers who share common vocabularies and experiences’ (Deeks in Va Law Rev 104, pp. 1529–1593, 2018). The example discussed here is recidivism prediction, where the factors that are of interest are difficult to capture adequately through questionnaires because survey responses do not necessarily indicate whether the behaviour that is of interest is present. There is room for improvement in how questions are phrased, in the selection of variables, and by encouraging practitioners to consider whether a particular variable is the sort of thing that can be measured by questionnaires at all.

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reprint Greene, Catherine (2023) "AI and the Social Sciences: Why All Variables are Not Created Equal". Res Publica 29(2):303-319

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Catherine Greene
London School of Economics

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References found in this work

The problem of variable choice.James Woodward - 2016 - Synthese 193 (4):1047-1072.
Nomadic Concepts, Variable Choice, and the Social Sciences.Catherine Greene - 2020 - Philosophy of the Social Sciences 50 (1):3-22.

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