Who Cited It

Definitions, methods, and applications in interpretable machine learning

2019 · Proceedings of the National Academy of Sciences · 2,161 citations · 4 from inside this corpus

William J. Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, Bin Yu

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

Definitions, methods, and applications in interpretable machine learning (2019)Definitions, methods, and app…Random Forests (2001)Random Forestsggplot2 (2016)ggplot2Classification and Regression Trees. (1986)Classification and Regression…ggplot2: Elegant Graphics for Data Analysis (2009)ggplot2: Elegant Graphics for…"Why Should I Trust You?" (2016)"Why Should I Trust You?"A survey on deep learning in medical image analysis (2017)A survey on deep learning in …Advances in Neural Information Processing Systems 28 (2015)Advances in Neural Informatio…Advances in neural information processing systems 7 (1996)Advances in neural informatio…On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Deg… (2024)On a Method to Measure Superv…Advances in Neural Information Processing Systems 29 (2016)Advances in Neural Informatio…Multimodel Inference (2004)Multimodel InferenceData Structures for Statistical Computing in Python (2010)Data Structures for Statistic…Stop explaining black box machine learning models for high stakes decisions and use inter… (2019)Stop explaining black box mac…A Unified Approach to Interpreting Model Predictions (2017)A Unified Approach to Interpr…Probabilistic graphical models : principles and techniques (2009)Probabilistic graphical model…“Why Should I Trust You?”: Explaining the Predictions of Any Classifier (2016)“Why Should I Trust You?”: Ex…A survey of methods for explaining black box models (2019)A survey of methods for expla…Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) (2001)Statistical Modeling: The Two…IPython: A System for Interactive Scientific Computing (2007)IPython: A System for Interac…Fairness through awareness (2012)Axiomatic Attribution for Deep Networks (2017)Axiomatic Attribution for Dee…Intelligible Models for HealthCare (2015)Intelligible Models for Healt…tidyverse: Easily Install and Load the 'Tidyverse' (2016)tidyverse: Easily Install and…Stop explaining black box machine learning models for high stakes decisions and use inter… (2019)Stop explaining black box mac…Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challe… (2019)Explainable Artificial Intell…Explainable AI: A Review of Machine Learning Interpretability Methods (2020)Explainable AI: A Review of M…Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustw… (2023)Explainable Artificial Intell…
27 of 27 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.
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What this paper cites, inside the corpus

PaperYearCited
Random Forests2001131,109
ggplot2201633,962
Classification and Regression Trees.198621,044
ggplot2: Elegant Graphics for Data Analysis200920,582
"Why Should I Trust You?"201616,210
A survey on deep learning in medical image analysis201715,110
Advances in Neural Information Processing Systems 28201514,672
Advances in neural information processing systems 7199614,393
On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Deg…202413,528
Advances in Neural Information Processing Systems 29201613,474
Multimodel Inference200411,781
Data Structures for Statistical Computing in Python201011,654
Stop explaining black box machine learning models for high stakes decisions and use inter…20199,924
A Unified Approach to Interpreting Model Predictions20177,625
Probabilistic graphical models : principles and techniques20096,450
“Why Should I Trust You?”: Explaining the Predictions of Any Classifier20165,480
A survey of methods for explaining black box models20194,992
Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)20014,362
IPython: A System for Interactive Scientific Computing20074,205
Fairness through awareness20123,476
Axiomatic Attribution for Deep Networks20172,650
Intelligible Models for HealthCare20151,693
tidyverse: Easily Install and Load the 'Tidyverse'20161,483

What cites it, inside the corpus

PaperYearCited
Stop explaining black box machine learning models for high stakes decisions and use inter…20199,924
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challe…20199,796
Explainable AI: A Review of Machine Learning Interpretability Methods20202,907
Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustw…20231,679

Links

DOI · Open access copy · OpenAlex record

Topics

Explainable Artificial Intelligence (XAI)Computer Science
Adversarial Robustness in Machine LearningComputer Science
Machine Learning and Data ClassificationComputer Science

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

  • supports5 author record(s) attached.
  • supports137 reference(s) recorded.
  • neutralThe DOI carries no year to check against.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:50+00:00.

sha256 86a6d6715ff540a0…