.png){kind=icon}
Return
Understanding from Machine Learning Models
.png){kind=icon}
OA
AI
DOI：10.1093/bjps/axz035.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models provide understanding misguided? In this article, using the case of deep neural networks, I argue that it is not the complexity or black box nature of a model that limits how much understanding the model provides. Instead, it is a lack of scientific and empirical evidence supporting the link that connects a model to the target phenomenon that primarily prohibits understanding.
Keywords:
RESIDENTIAL PREFERENCES
SEXUAL ORIENTATION
VALUES
SEGREGATION
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.7
Papers:
2.2K
Citations:
6.7K
Organization
Cited Papers
Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records.png)
SCIENTIFIC REPORTSOAAI
SCIENTIFIC REPORTSIF3.9
OAAIShareSaveShareSaveAttitudes on residential integration: Perceived status differences, mere in-group preference, or racial prejudice?SOCIAL FORCES
SOCIAL FORCESIF2.7
PREAI
ShareSaveShareSaveShareSaveShareSaveShareSave
View more