Who Cited It

"Why Should I Trust You?"

2016 · 16,210 citations · 20 from inside this corpus

Marco Túlio Ribeiro, Sameer Singh, Carlos Guestrin

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.

"Why Should I Trust You?" (2016)"Why Should I Trust You?"Scikit-learn: Machine Learning in Python (2012)Scikit-learn: Machine Learnin…Distributed Representations of Words and Phrases and their Compositionality (2013)Distributed Representations o…The magical number seven, plus or minus two: Some limits on our capacity for processing i… (1994)The magical number seven, plu…Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classifi… (2007)Biographies, Bollywood, Boom-…Intelligible Models for HealthCare (2015)From local explanations to global understanding with explainable AI for trees (2020)From local explanations to gl…Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challe… (2019)Explainable Artificial Intell…A Unified Approach to Interpreting Model Predictions (2017)A Unified Approach to Interpr…Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) (2018)Peeking Inside the Black-Box:…“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…Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks (2018)Grad-CAM++: Generalized Gradi…Deep learning for healthcare: review, opportunities and challenges (2017)Deep learning for healthcare:…Explainable AI: A Review of Machine Learning Interpretability Methods (2020)Explainable AI: A Review of M…Methods for interpreting and understanding deep neural networks (2017)Methods for interpreting and …A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2020)A Survey on Explainable Artif…Definitions, methods, and applications in interpretable machine learning (2019)Definitions, methods, and app…Anchors: High-Precision Model-Agnostic Explanations (2018)Anchors: High-Precision Model…Shortcut learning in deep neural networks (2020)Shortcut learning in deep neu…Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2023)Interpreting Black-Box Models…Causability and explainability of artificial intelligence in medicine (2019)Causability and explainabilit…Machine Learning Interpretability: A Survey on Methods and Metrics (2019)Machine Learning Interpretabi…Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustw… (2023)Explainable Artificial Intell…Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications (2021)Explaining Deep Neural Networ…Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks (2020)Score-CAM: Score-Weighted Vis…
25 of 25 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
Scikit-learn: Machine Learning in Python201264,025
Distributed Representations of Words and Phrases and their Compositionality201318,054
The magical number seven, plus or minus two: Some limits on our capacity for processing i…199417,324
Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classifi…20072,031
Intelligible Models for HealthCare20151,693

What cites it, inside the corpus

PaperYearCited
From local explanations to global understanding with explainable AI for trees20209,936
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challe…20199,796
A Unified Approach to Interpreting Model Predictions20177,625
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)20186,166
“Why Should I Trust You?”: Explaining the Predictions of Any Classifier20165,480
A survey of methods for explaining black box models20194,992
Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks20183,219
Deep learning for healthcare: review, opportunities and challenges20173,115
Explainable AI: A Review of Machine Learning Interpretability Methods20202,907
Methods for interpreting and understanding deep neural networks20172,763
A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI20202,377
Definitions, methods, and applications in interpretable machine learning20192,161
Anchors: High-Precision Model-Agnostic Explanations20182,124
Shortcut learning in deep neural networks20202,040
Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence20231,981
Causability and explainability of artificial intelligence in medicine20191,851
Machine Learning Interpretability: A Survey on Methods and Metrics20191,822
Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustw…20231,679
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications20211,424
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks20201,378

Links

DOI · OpenAlex record

Topics

Explainable Artificial Intelligence (XAI)Computer Science
Adversarial Robustness in Machine LearningComputer Science
Machine Learning in HealthcareComputer Science

Is this record sound?

complete

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

  • supports3 author record(s) attached.
  • supports38 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 2026-09-04T03:58:40+00:00.

sha256 7e3d99a592f7f61f…