Radical empiricism and machine learning research
I contrast the “data fitting” vs “data interpreting” approaches to data science along three dimensions: Expediency, Transparency, and Explainability. “Data fitting” is driven by the faith that the secret to rational decisions lies in the data itself. In contrast, the data-interpreting school views d...
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| Format: | Article |
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De Gruyter
2021-05-01
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| Series: | Journal of Causal Inference |
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| Online Access: | https://doi.org/10.1515/jci-2021-0006 |
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doaj-143d027ed3ea43e9b1bd71334af9df052021-10-03T07:42:34ZengDe GruyterJournal of Causal Inference2193-36852021-05-0191788210.1515/jci-2021-0006Radical empiricism and machine learning researchPearl Judea0University of California, Los Angeles, Computer Science Department, Los Angeles, CA, 90095-1596, United States of AmericaI contrast the “data fitting” vs “data interpreting” approaches to data science along three dimensions: Expediency, Transparency, and Explainability. “Data fitting” is driven by the faith that the secret to rational decisions lies in the data itself. In contrast, the data-interpreting school views data, not as a sole source of knowledge but as an auxiliary means for interpreting reality, and “reality” stands for the processes that generate the data. I argue for restoring balance to data science through a task-dependent symbiosis of fitting and interpreting, guided by the Logic of Causation.https://doi.org/10.1515/jci-2021-0006causal modelsknowledge representationmachine learning |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Pearl Judea |
| spellingShingle |
Pearl Judea Radical empiricism and machine learning research Journal of Causal Inference causal models knowledge representation machine learning |
| author_facet |
Pearl Judea |
| author_sort |
Pearl Judea |
| title |
Radical empiricism and machine learning research |
| title_short |
Radical empiricism and machine learning research |
| title_full |
Radical empiricism and machine learning research |
| title_fullStr |
Radical empiricism and machine learning research |
| title_full_unstemmed |
Radical empiricism and machine learning research |
| title_sort |
radical empiricism and machine learning research |
| publisher |
De Gruyter |
| series |
Journal of Causal Inference |
| issn |
2193-3685 |
| publishDate |
2021-05-01 |
| description |
I contrast the “data fitting” vs “data interpreting” approaches to data science along three dimensions: Expediency, Transparency, and Explainability. “Data fitting” is driven by the faith that the secret to rational decisions lies in the data itself. In contrast, the data-interpreting school views data, not as a sole source of knowledge but as an auxiliary means for interpreting reality, and “reality” stands for the processes that generate the data. I argue for restoring balance to data science through a task-dependent symbiosis of fitting and interpreting, guided by the Logic of Causation. |
| topic |
causal models knowledge representation machine learning |
| url |
https://doi.org/10.1515/jci-2021-0006 |
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AT pearljudea radicalempiricismandmachinelearningresearch |
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