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ERIC Number: ED634088
Record Type: Non-Journal
Publication Date: 2022-Oct-22
Pages: 19
Abstractor: As Provided
ISBN: N/A
ISSN: N/A
EISSN: N/A
Toward a Taxonomy of Trust for Probabilistic Machine Learning
Tamara Broderick; Andrew Gelman; Rachael Meager; Anna L. Smith; Tian Zheng
Grantee Submission
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: (1) in the translation of real-world goals to goals on a particular set of training data, (2) in the translation of abstract goals on the training data to a concrete mathematical problem, (3) in the use of an algorithm to solve the stated mathematical problem, and (4) 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 steps where existing research work on trust tends to concentrate and also steps where building trust is particularly challenging. [This paper was published in "Science Advances."]
Publication Type: Reports - Descriptive
Education Level: N/A
Audience: N/A
Language: English
Sponsor: National Science Foundation (NSF); Office of Naval Research (ONR) (DOD); Institute of Education Sciences (ED); National Institutes of Health (NIH) (DHHS)
Authoring Institution: N/A
IES Funded: Yes
Grant or Contract Numbers: R305D190048
Data File: URL: N/A