Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information | Paper Citation Record

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This paper cites ”why should i trust you?”: Explaining the predictions of any classifier.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information ”why should i trust you?”: Explaining the predictions of any classifier

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This paper cites Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra

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This paper cites Cats and Dogs Classification Dataset.https://www.kaggle.com/datasets/ bhavikjikadara/dog-and-cat-classification-dataset, 2024.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Cats and Dogs Classification Dataset.https://www.kaggle.com/datasets/ bhavikjikadara/dog-and-cat-classification-dataset, 2024

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Axiomatic attribution for deep networks

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This paper cites Counterfactual explanations without opening the black box: Automated decisions and the gdpr.Harv.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Counterfactual explanations without opening the black box: Automated decisions and the gdpr.Harv

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This paper cites Mechanistic Interpretability for AI Safety - A Review.Transac- tions on Machine Learning Research, 2024.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Mechanistic Interpretability for AI Safety - A Review.Transac- tions on Machine Learning Research, 2024

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This paper cites Lundberg and Su-In Lee.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Lundberg and Su-In Lee

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Beyond individualized recourse: Interpretable and interac- tive summaries of actionable recourses

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Investigating hiring bias in large language models

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information SmoothGrad: removing noise by adding noise

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Learning important features through propagating activation differences

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Using the adap learning algorithm to forecast the onset of diabetes mellitus

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Dropout: A simple way to prevent neural networks from overfitting.Journal of Machine Learning Research, 15(56):1929–1958, 2014

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Searching for mobilenetv3

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Legal judgment reimagined: PredEx and the rise of intelligent AI interpretation in Indian courts

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Using ”annotator rationales” to improve machine learning for text categorization

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Unresolved cited work

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Amazon puts its own “brands” first above better-rated products.The Markup, October 2021

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This paper cites Explainable ai in industry: Practical challenges and lessons learned.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Explainable ai in industry: Practical challenges and lessons learned

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This paper cites URL https://docs.arize.com/arize/machine-learning/ how-to-ml/explainability/surrogate-model.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information URL https://docs.arize.com/arize/machine-learning/ how-to-ml/explainability/surrogate-model

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This paper cites URL https://docs.fiddler.ai/ui-guide/ explainability-ui-giude/surrogate-models.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information URL https://docs.fiddler.ai/ui-guide/ explainability-ui-giude/surrogate-models

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information URL https://learn.microsoft.com/en-us/azure/machine-learning/ concept-model-interpretability

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Calmon, and Mario Diaz

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Amazing things come from having many good models,

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Understanding prediction discrepancies in classification.Machine Learning, Aug 2024

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Predictive multiplicity in classification

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Amazing Things Come From Having Many Good Models

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Unresolved cited work

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information An empirical evaluation of the rashomon effect in explainable machine learning

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information ISBN 978-3-031-43418-1

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Duarte, and Jochen Garcke

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Esterhuizen, Bryan R

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Machine learning- assisted study of ren(x)c(6-x)-doped graphene as potential electrocatalysts for oxygen electrode reactions

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information doi: https://doi.org/10.1016/j.scitotenv.2023.166662

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