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F. Fischer:
"The Accuracy Paradox of Algorithmic Classification";
Vortrag: 18th Annual STS Conference Graz 2019: Critical Issues in Science, Technology and Society Studies, Graz; 06.05.2019 - 07.05.2019; in: "Conference Proceedings of the STS Conference Graz 2019, Critical Issues in Science, Technology and Society Studies, 6 - 7 May 2019", Verlag der Technischen Universität Graz, Graz (2019), ISBN: 978-3-85125-668-0; S. 105 - 120.

In recent years, algorithmic classification based on machine learning techniques has been increasingly permeating our lives. With their increased ubiquity, negative social consequences have come to light. Among these consequences are ´unfair´ algorithms. This resulted in a large body of research tackling ´fairness´ of algorithms and related issues. Algorithms are frequently considered as unfair if they show diverging accuracies for different groups, with a particular focus on vulnerable groups, indicating a correlation between prediction and information about group membership.
In this paper I argue that, while this research contributes valuable insights, much of the research focuses a quantitative understanding of fairness which creates a very narrow focus. My argument builds on four pillars. First, much of the research on 'fairness' focuses on accuracy as basis for ´fairness´. Even though ´fairness´ can reduce the overall accuracy, this is seen as a limitation, implicitly aiming for high accuracy. Second, this focus is in line with other debates about algorithmic classification that focus on quantiative performance measures. Third, close attention on accuracy may be a pragmatic and well-intended stance for practicioners but can distract from problematizing the ´bigger picture´. Fourth, I argue that any classification produces a marginalized group, namely those that are misclassified. This marginalization increases with the classifier´s accuracy, and in tandem the ability of the affected to challenge the classification is diminished. Combined, this leads to the situation that a focus on fairness and accuracy may weaken the position and agency of those being misclassified, paradoxically contradicting the promissory narrative of ´fixing´ algorithms through optimizing fairness and accuracy.

governing algorithms, algorithmic classification, accuracy, agency, algorithmic decision making

http://dx.doi.org/10.3217/978-3-85125-668-0

https://publik.tuwien.ac.at/files/publik_283115.pdf