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Please add the below NeurIPS publication #5

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46 changes: 24 additions & 22 deletions README.md
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Expand Up @@ -14,26 +14,25 @@ Fairness in AI (FAI) aims to build fair and unbiased AI/machine learning systems

## Table of Contents

* [Review and General Papers](#review-and-general-papers)
* [Measurements of Fairness](#measurements-of-fairness)
* Demonstration of Bias Phemomenon in Various Applications
* [Bias in Machine Learning Models](#bias-in-machine-learning-models)
* [Bias in Representations](#bias-in-representations)
* Mitigation of Unfairness
* [Mitigation of Machine Learning Models](#mitigation-of-machine-learning-models)
* Adversarial Learning
* Calibration
* Incorporating Priors into Feature Attribution
* Data Collection
* Other Mitigation Methods
* [Mitigation of Representations](#mitigation-of-representations)
* [Fairness Packages and Frameworks](#fairness-packages-and-frameworks)
* [Conferences](#conferences)
* [Other Fairness Relevant Interpretability Resources](#other-fairness-relevant-interpretability-resources)
- [Awesome-Fairness-in-AI ![Awesome](https://github.com/sindresorhus/awesome)](#awesome-fairness-in-ai-)
- [What is Fairness in AI?](#what-is-fairness-in-ai)
- [Table of Contents](#table-of-contents)
- [Review and General Papers](#review-and-general-papers)
- [Measurements of Fairness](#measurements-of-fairness)
- [Demonstration of Bias Phemomenon in Various Applications](#demonstration-of-bias-phemomenon-in-various-applications)
- [Bias in Machine Learning Models](#bias-in-machine-learning-models)
- [Bias in Representations](#bias-in-representations)
- [Mitigation of Unfairness](#mitigation-of-unfairness)
- [Mitigation of Machine Learning Models](#mitigation-of-machine-learning-models)
- [Mitigation of Representations](#mitigation-of-representations)
- [Fairness Packages and Frameworks](#fairness-packages-and-frameworks)
- [Conferences](#conferences)
- [Other Fairness Relevant Interpretability Resources](#other-fairness-relevant-interpretability-resources)


## Review and General Papers

* [Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness Metrics](https://openreview.net/forum?id=OTnqQUEwPKu)
* [Fairness in Deep Learning: A Computational Perspective](https://arxiv.org/pdf/1908.08843.pdf)
* [The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning](https://arxiv.org/pdf/1808.00023.pdf)
* [Fairness and machine learning](https://fairmlbook.org/)
Expand All @@ -60,6 +59,7 @@ Fairness in AI (FAI) aims to build fair and unbiased AI/machine learning systems
* [Bias in data‐driven artificial intelligence systems—An introductory survey](https://onlinelibrary.wiley.com/doi/full/10.1002/widm.1356)
* [Fairness is not Static: Deeper Understanding of Long Term Fairness via Simulation Studies](https://github.com/google/ml-fairness-gym/blob/master/papers/acm_fat_2020_fairness_is_not_static.pdf)
* [Delayed Impact of Fair Machine Learning](http://proceedings.mlr.press/v80/liu18c/liu18c.pdf)
* [Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness Metrics](https://openreview.net/forum?id=OTnqQUEwPKu)

## Demonstration of Bias Phemomenon in Various Applications
### Bias in Machine Learning Models
Expand All @@ -69,14 +69,14 @@ Fairness in AI (FAI) aims to build fair and unbiased AI/machine learning systems
* [Demographic Dialectal Variation in Social Media: A Case Study of African-American English](https://aclweb.org/anthology/D16-1120/)
* [Feature-Wise Bias Amplification](https://arxiv.org/pdf/1812.08999.pdf)
* [ConvNets and ImageNet Beyond Accuracy: Understanding Mistakes and Uncovering Biases](https://arxiv.org/pdf/1711.11443.pdf)

* [Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness Metrics](https://openreview.net/forum?id=OTnqQUEwPKu)


### Bias in Representations
* [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://arxiv.org/pdf/1607.06520.pdf)
* [Gender Bias in Contextualized Word Embeddings](https://arxiv.org/pdf/1904.03310.pdf)
* [Assessing Social and Intersectional Biases in Contextualized Word Representations](http://papers.nips.cc/paper/9479-assessing-social-and-intersectional-biases-in-contextualized-word-representations.pdf)

* [Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness Metrics](https://openreview.net/forum?id=OTnqQUEwPKu)


## Mitigation of Unfairness
Expand All @@ -89,6 +89,8 @@ Fairness in AI (FAI) aims to build fair and unbiased AI/machine learning systems
* [Mitigating Unwanted Biases with Adversarial Learning](https://arxiv.org/pdf/1801.07593.pdf)
* [Adversarial Removal of Demographic Attributes from Text Data](https://arxiv.org/pdf/1808.06640.pdf)
* [Compositional Fairness Constraints for Graph Embeddings](http://proceedings.mlr.press/v97/bose19a/bose19a.pdf)
* [Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness Metrics](https://openreview.net/forum?id=OTnqQUEwPKu)


* Calibration
* [Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints](https://arxiv.org/pdf/1707.09457.pdf)
Expand All @@ -107,7 +109,7 @@ Fairness in AI (FAI) aims to build fair and unbiased AI/machine learning systems
* [Why Is My Classifier Discriminatory?](https://papers.nips.cc/paper/7613-why-is-my-classifier-discriminatory.pdf)
* [Incorporating Dialectal Variability for Socially Equitable Language Identification](https://www.aclweb.org/anthology/P17-2009/)
* [REPAIR: Removing Representation Bias by Dataset Resampling](http://openaccess.thecvf.com/content_CVPR_2019/papers/Li_REPAIR_Removing_Representation_Bias_by_Dataset_Resampling_CVPR_2019_paper.pdf)

* [Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness Metrics](https://openreview.net/forum?id=OTnqQUEwPKu)



Expand All @@ -131,7 +133,7 @@ Fairness in AI (FAI) aims to build fair and unbiased AI/machine learning systems
* [Learning Gender-Neutral Word Embeddings](https://arxiv.org/pdf/1809.01496.pdf)
* [Flexibly Fair Representation Learning by Disentanglement](https://arxiv.org/pdf/1906.02589.pdf)
* [Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings](https://arxiv.org/pdf/1809.02169.pdf)

* [Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness Metrics](https://openreview.net/forum?id=OTnqQUEwPKu)



Expand All @@ -151,7 +153,7 @@ Fairness in AI (FAI) aims to build fair and unbiased AI/machine learning systems
* [fairness-indicators: Tensorflow's Fairness Evaluation and Visualization Toolkit](https://github.com/tensorflow/fairness-indicators)
* [scikit-fairness](https://github.com/koaning/scikit-fairness)
* [Mitigating Gender Bias In Captioning System](https://github.com/CaptionGenderBias2020/Mitigating_Gender_Bias_In_Captioning_System_NIPS2020)

* [Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness Metrics](https://github.com/charan223/FairDeepLearning)

## Conferences

Expand All @@ -167,7 +169,7 @@ Fairness in AI (FAI) aims to build fair and unbiased AI/machine learning systems
* [Towards A Rigorous Science of Interpretable Machine Learning](https://arxiv.org/pdf/1702.08608.pdf)
* [Techniques for Interpretable Machine Learning](https://arxiv.org/pdf/1808.00033.pdf)
* [Methods for Interpreting and Understanding Deep Neural Networks](https://arxiv.org/pdf/1706.07979.pdf)

* [Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness Metrics](https://openreview.net/forum?id=OTnqQUEwPKu)



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