Fairness in Deep Learning: A Computational Perspective | Paper Citation Record

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This paper cites On formalizing fairness in pre- diction with machine learning,.

Fairness in Deep Learning: A Computational Perspective On formalizing fairness in pre- diction with machine learning,

Reference 1

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This paper cites Bal- anced datasets are not enough: Estimating and mitigating gender bias in deep image representations,.

Fairness in Deep Learning: A Computational Perspective Bal- anced datasets are not enough: Estimating and mitigating gender bias in deep image representations,

Reference 2

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This paper cites Examining gender and race bias in two hundred sentiment analysis systems,.

Fairness in Deep Learning: A Computational Perspective Examining gender and race bias in two hundred sentiment analysis systems,

Reference 3

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This paper cites Gender shades: Intersectional accu- racy disparities in commercial gender classiﬁcation,.

Fairness in Deep Learning: A Computational Perspective Gender shades: Intersectional accu- racy disparities in commercial gender classiﬁcation,

Reference 4

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This paper cites Annotation artifacts in natural lan- guage inference data,.

Fairness in Deep Learning: A Computational Perspective Annotation artifacts in natural lan- guage inference data,

Reference 5

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This paper cites Man is to computer programmer as woman is to homemaker? debiasing word embeddings,.

Fairness in Deep Learning: A Computational Perspective Man is to computer programmer as woman is to homemaker? debiasing word embeddings,

Reference 6

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This paper cites Men also like shopping: Reducing gender bias ampliﬁcation using corpus-level constraints,.

Fairness in Deep Learning: A Computational Perspective Men also like shopping: Reducing gender bias ampliﬁcation using corpus-level constraints,

Reference 7

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This paper cites Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination.

Fairness in Deep Learning: A Computational Perspective Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination

Reference 8

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This paper cites Data decisions and theoretical implications when adversarially learning fair repre- sentations,.

Fairness in Deep Learning: A Computational Perspective Data decisions and theoretical implications when adversarially learning fair repre- sentations,

Reference 9

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This paper cites Demographic dialectal variation in social media: A case study of african-american en- glish,.

Fairness in Deep Learning: A Computational Perspective Demographic dialectal variation in social media: A case study of african-american en- glish,

Reference 10

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This paper cites Incorporating dialectal variability for socially equitable language identiﬁcation,.

Fairness in Deep Learning: A Computational Perspective Incorporating dialectal variability for socially equitable language identiﬁcation,

Reference 11

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Observation 68ff9aee-bf07-40cd-a541-231d7c9e63bb · outbound

This paper cites Can ai help reduce disparities in general medical and mental health care?.

Fairness in Deep Learning: A Computational Perspective Can ai help reduce disparities in general medical and mental health care?

Reference 12

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This paper cites Auditing ImageNet: Towards a Model-driven Framework for Annotating Demographic Attributes of Large-Scale Image Datasets.

Fairness in Deep Learning: A Computational Perspective Auditing ImageNet: Towards a Model-driven Framework for Annotating Demographic Attributes of Large-Scale Image Datasets

Reference 13

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This paper cites Fairness through awareness,.

Fairness in Deep Learning: A Computational Perspective Fairness through awareness,

Reference 14

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This paper cites Putting fairness principles into practice: Challenges, metrics, and improvements,.

Fairness in Deep Learning: A Computational Perspective Putting fairness principles into practice: Challenges, metrics, and improvements,

Reference 15

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This paper cites Certifying and removing disparate im- pact,.

Fairness in Deep Learning: A Computational Perspective Certifying and removing disparate im- pact,

Reference 16

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Observation f354c162-7cf9-4b60-9532-74ef72faf89f · outbound

This paper cites Equality of opportunity in supervised learning,.

Fairness in Deep Learning: A Computational Perspective Equality of opportunity in supervised learning,

Reference 17

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This paper cites Techniques for interpretable machine learning,.

Fairness in Deep Learning: A Computational Perspective Techniques for interpretable machine learning,

Reference 18

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Observation 22a92c4d-a27c-461d-8a85-2a1062c27708 · outbound

This paper cites Towards explanation of dnn- based prediction with guided feature inversion,.

Fairness in Deep Learning: A Computational Perspective Towards explanation of dnn- based prediction with guided feature inversion,

Reference 19

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Observation 1b82baf0-24e0-41a5-9c82-c246b2964d8e · outbound

This paper cites On attribution of recurrent neural network predictions via additive decomposition,.

Fairness in Deep Learning: A Computational Perspective On attribution of recurrent neural network predictions via additive decomposition,

Reference 20

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This paper cites Un- derstanding neural networks through deep visualization,.

Fairness in Deep Learning: A Computational Perspective Un- derstanding neural networks through deep visualization,

Reference 21

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This paper cites Interpretability beyond feature attribution: Quantita- tive testing with concept activation vectors (tcav),.

Fairness in Deep Learning: A Computational Perspective Interpretability beyond feature attribution: Quantita- tive testing with concept activation vectors (tcav),

Reference 22

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This paper cites Certifai: A common framework to provide explanations and analyse the fairness and robustness of black-box models,.

Fairness in Deep Learning: A Computational Perspective Certifai: A common framework to provide explanations and analyse the fairness and robustness of black-box models,

Reference 23

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This paper cites Interpretable basis decomposition for visual explanation,.

Fairness in Deep Learning: A Computational Perspective Interpretable basis decomposition for visual explanation,

Reference 24

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This paper cites Net2vec: Quantifying and explaining how concepts are encoded by ﬁlters in deep neural networks,.

Fairness in Deep Learning: A Computational Perspective Net2vec: Quantifying and explaining how concepts are encoded by ﬁlters in deep neural networks,

Reference 25

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This paper cites Learning deep features for discriminative localization,.

Fairness in Deep Learning: A Computational Perspective Learning deep features for discriminative localization,

Reference 26

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Observation 61a205cb-a4e8-47ed-bf7b-5d716744c88b · outbound

This paper cites Deep Learning for Face Recognition: Pride or Prejudiced?.

Fairness in Deep Learning: A Computational Perspective Deep Learning for Face Recognition: Pride or Prejudiced?

Reference 27

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This paper cites Right for the right reasons: Training differentiable models by constraining their explanations,.

Fairness in Deep Learning: A Computational Perspective Right for the right reasons: Training differentiable models by constraining their explanations,

Reference 28

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This paper cites Incorporating priors with feature attribution on text classiﬁcation,.

Fairness in Deep Learning: A Computational Perspective Incorporating priors with feature attribution on text classiﬁcation,

Reference 29

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This paper cites A reductions approach to fair classiﬁcation,.

Fairness in Deep Learning: A Computational Perspective A reductions approach to fair classiﬁcation,

Reference 30

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This paper cites Data preprocessing techniques for classiﬁcation without discrimination,.

Fairness in Deep Learning: A Computational Perspective Data preprocessing techniques for classiﬁcation without discrimination,

Reference 31

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This paper cites Fairness- aware classiﬁer with prejudice remover regularizer,.

Fairness in Deep Learning: A Computational Perspective Fairness- aware classiﬁer with prejudice remover regularizer,

Reference 32

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This paper cites Optimized pre-processing for discrimination preven- tion,.

Fairness in Deep Learning: A Computational Perspective Optimized pre-processing for discrimination preven- tion,

Reference 33

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This paper cites Adversarial removal of demographic attributes from text data,.

Fairness in Deep Learning: A Computational Perspective Adversarial removal of demographic attributes from text data,

Reference 34

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This paper cites Learning adver- sarially fair and transferable representations,.

Fairness in Deep Learning: A Computational Perspective Learning adver- sarially fair and transferable representations,

Reference 35

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This paper cites Discovering fair representations in the data domain,.

Fairness in Deep Learning: A Computational Perspective Discovering fair representations in the data domain,

Reference 36

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This paper cites The Variational Fair Autoencoder.

Fairness in Deep Learning: A Computational Perspective The Variational Fair Autoencoder

Reference 37

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This paper cites Wasserstein Fair Classification.

Fairness in Deep Learning: A Computational Perspective Wasserstein Fair Classification

Reference 38

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This paper cites Chalearn looking at people and faces of the world: Face analysis workshop and challenge 2016,.

Fairness in Deep Learning: A Computational Perspective Chalearn looking at people and faces of the world: Face analysis workshop and challenge 2016,

Reference 39

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This paper cites Inclusivefacenet: Improving face attribute detection with race and gender diversity,.

Fairness in Deep Learning: A Computational Perspective Inclusivefacenet: Improving face attribute detection with race and gender diversity,

Reference 40

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This paper cites Age progression/regression by conditional adversarial autoencoder,.

Fairness in Deep Learning: A Computational Perspective Age progression/regression by conditional adversarial autoencoder,

Reference 41

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This paper cites Mitigating bias in gender, age and ethnicity classiﬁcation: a multi-task convolution neural network approach,.

Fairness in Deep Learning: A Computational Perspective Mitigating bias in gender, age and ethnicity classiﬁcation: a multi-task convolution neural network approach,

Reference 42

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This paper cites AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias.

Fairness in Deep Learning: A Computational Perspective AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias

Reference 43

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This paper cites Learning credible deep neu- ral networks with rationale regularization,.

Fairness in Deep Learning: A Computational Perspective Learning credible deep neu- ral networks with rationale regularization,

Reference 44

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This paper cites Domain- adversarial training of neural networks,.

Fairness in Deep Learning: A Computational Perspective Domain- adversarial training of neural networks,

Reference 45

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This paper cites Synthetic data augmentation using gan for im- proved liver lesion classiﬁcation,.

Fairness in Deep Learning: A Computational Perspective Synthetic data augmentation using gan for im- proved liver lesion classiﬁcation,

Reference 46

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This paper cites Compositional fairness constraints for graph embeddings,.

Fairness in Deep Learning: A Computational Perspective Compositional fairness constraints for graph embeddings,

Reference 47

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This paper cites Flexibly fair representation learning by disentanglement,.

Fairness in Deep Learning: A Computational Perspective Flexibly fair representation learning by disentanglement,

Reference 48

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This paper cites Bert: Pre- training of deep bidirectional transformers for language under- standing,.

Fairness in Deep Learning: A Computational Perspective Bert: Pre- training of deep bidirectional transformers for language under- standing,

Reference 49

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This paper cites Xlnet: Generalized autoregressive pretraining for language understanding,.

Fairness in Deep Learning: A Computational Perspective Xlnet: Generalized autoregressive pretraining for language understanding,

Reference 50

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