Mussgnug, Alexander (2022) The Predictive Reframing of Machine Learning Applications: Good Predictions and Bad Measurements. In: UNSPECIFIED.

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Abstract

Supervised machine learning has found its way into ever more areas of scientific inquiry, where the outcomes of supervised machine learning applications are almost universally classified as predictions. I argue that what researchers often present as a mere terminological particularity of the field involves the consequential transformation of tasks as diverse as classification, measurement, or image segmentation into prediction problems. Focusing on the case of machine-learning enabled poverty prediction, I explore how reframing a measurement problem as a prediction task alters the primary epistemic aim of the application. Instead of measuring a property, machine learning developers conceive of their models as predicting a given measurement of this property. I argue that this predictive reframing common to supervised machine learning applications is epistemically and ethically problematic, as it allows developers to externalize concerns critical to the epistemic validity and ethical implications of their model's inferences. I further hold that the predictive reframing is not a necessary feature of supervised machine learning by offering an alternative conception of machine learning models as measurement models. An interpretation of supervised machine learning applications to measurement tasks as automatically-calibrated model-based measurements internalizes questions of construct validity and ethical desirability critical to the measurement problem these applications are intended to and presented as solving. Thereby, this paper introduces an initial framework for exploring technical, historical, and philosophical research at the intersection of measurement and machine learning.

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Item Type:	Conference or Workshop Item (UNSPECIFIED)
Creators:	Creators Email ORCID Mussgnug, Alexander Martin 0000-0002-5951-057X
Keywords:	Machine Learning Measurement Prediction Modeling Conceptual Engineering
Subjects:	General Issues > Data Specific Sciences > Artificial Intelligence General Issues > Ethical Issues Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Technology
Depositing User:	Mr Alexander Mussgnug
Date Deposited:	27 Jun 2022 18:09
Last Modified:	27 Jun 2022 18:09
Item ID:	20809
Subjects:	General Issues > Data Specific Sciences > Artificial Intelligence General Issues > Ethical Issues Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Technology
Date:	2022
URI:	https://philsci-archive.pitt.edu/id/eprint/20809

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• The Predictive Reframing of Machine Learning Applications: Good Predictions and Bad Measurements. (deposited 27 Jun 2022 18:09) [Currently Displayed]
  • The Predictive Reframing of Machine Learning Applications: Good Predictions and Bad Measurements. (deposited 26 Jul 2022 19:51)

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