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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead paper by C. Rudin
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Paper

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

by C. Rudin arxiv-paper--unknown--bc00ff34ec7772080c7039b17f7069a2f7df0889
Nexus Index
65.1 Top 100%
S: Semantic 50
A: Authority 89
P: Popularity 73
R: Recency 100
Q: Quality 45
Tech Context
Vital Performance
0 DL / 30D
0.0%
High Impact 0 Citations
2024 Year
ArXiv Venue
- FNI Rank
Paper Information Summary
Entity Passport
Registry ID arxiv-paper--unknown--bc00ff34ec7772080c7039b17f7069a2f7df0889
License ArXiv
Provider semantic_scholar
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Cite this paper

Academic & Research Attribution

BibTeX 
@misc{arxiv_paper__unknown__bc00ff34ec7772080c7039b17f7069a2f7df0889,
  author = {C. Rudin},
  title = {Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead Paper},
  year = {2026},
  howpublished = {\url{https://free2aitools.com/paper/arxiv-paper--unknown--bc00ff34ec7772080c7039b17f7069a2f7df0889}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
C. Rudin. (2026). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead [Paper]. Free2AITools. https://free2aitools.com/paper/arxiv-paper--unknown--bc00ff34ec7772080c7039b17f7069a2f7df0889

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⚖ī¸ Nexus Index V2.0

Methodology Index Protocol
65.1
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 89
Popularity (P) 73
Recency (R) 100
Quality (Q) 45

đŸ’Ŧ Index Insight

FNI V2.0 for Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead: Semantic (S:50), Authority (A:89), Popularity (P:73), Recency (R:100), Quality (Q:45).

Free2AITools Nexus Index

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"Technical abstract for this publication is currently being indexed."

❝ Cite Node 

@article{Unknown2026Stop,
  title={Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead},
  author={},
  journal={arXiv preprint arXiv:arxiv-paper--unknown--bc00ff34ec7772080c7039b17f7069a2f7df0889},
  year={2026}
}

Abstract & Analysis

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Technical metadata sourced from upstream repositories.

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🆔 Identity & Source

id
arxiv-paper--unknown--bc00ff34ec7772080c7039b17f7069a2f7df0889
slug
unknown--bc00ff34ec7772080c7039b17f7069a2f7df0889
source
semantic_scholar
author
C. Rudin
license
ArXiv
tags
paper, research, academic

⚙ī¸ Technical Specs

architecture
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params billions
null
context length
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