AI and the social sciences: why all variables are not created equal

Greene, Catherine (2022) AI and the social sciences: why all variables are not created equal. Res Publica. ISSN 1356-4765

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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.

Item Type:	Article
Official URL:	https://www.springer.com/journal/11158
Additional Information:	© 2022 The Author
Divisions:	CPNSS
Subjects:	H Social Sciences > H Social Sciences (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date Deposited:	19 Jan 2022 11:54
Last Modified:	12 Dec 2024 02:48
URI:	http://eprints.lse.ac.uk/id/eprint/113462

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