Toward a taxonomy of trust for probabilistic machine learning

Broderick, Tamara, Gelman, Andrew, Meager, Rachael, Smith, Anna L. and Zheng, Tian (2023) Toward a taxonomy of trust for probabilistic machine learning. Science Advances, 9 (7). ISSN 2375-2548

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Abstract

Probabilistic machine learning increasingly informs critical decisions in medicine, economics, politics, and beyond. To aid the development of trust in these decisions, we develop a taxonomy delineating where trust in an analysis can break down: (i) in the translation of real-world goals to goals on a particular set of training data, (ii) in the translation of abstract goals on the training data to a concrete mathematical problem, (iii) in the use of an algorithm to solve the stated mathematical problem, and (iv) in the use of a particular code implementation of the chosen algorithm. We detail how trust can fail at each step and illustrate our taxonomy with two case studies. Finally, we describe a wide variety of methods that can be used to increase trust at each step of our taxonomy. The use of our taxonomy highlights not only steps where existing research work on trust tends to concentrate and but also steps where building trust is particularly challenging.

Item Type:	Article
Additional Information:	© 2023 The Author(s).
Divisions:	Economics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date Deposited:	01 Mar 2023 15:48
Last Modified:	18 Nov 2024 18:09
URI:	http://eprints.lse.ac.uk/id/eprint/118302

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