Fetching the paper…
Reading the bibliography…
Ensuring a neural network is not relying on protected attributes (e.g., race, sex, age) for predictions is crucial in advancing fair and trustworthy AI.
Using the adap learning algorithm to forecast the onset of diabetes mellitus
Jack W Smith, James E Everhart, WC Dickson, William C Knowler, and Robert Scott Johannes · 1988
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
The social construction of difference and inequality
Tracy E Ore and Paul Kurtz · 2000
Earlier work this paper cites.
Elements of information theory
Thomas M Cover and Joy A Thomas · 2006
Earlier work this paper cites.
Uci machine learning repository, 2007
Arthur Asuncion and David Newman · 2007
Earlier work this paper cites.
Building classifiers with independency constraints
Toon Calders, Faisal Kamiran, and Mykola Pechenizkiy · 2009
Earlier work this paper cites.
Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
Earlier work this paper cites.
Bi-shifting auto-encoder for unsupervised domain adaptation
Meina Kan, Shiguang Shan, and Xilin Chen · 2015
Earlier work this paper cites.
Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Earlier work this paper cites.
Deep learning and the information bottleneck principle
Naftali Tishby and Noga Zaslavsky · 2015
Earlier work this paper cites.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Inherent trade-offs in the fair determination of risk scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2016
Earlier work this paper cites.
Counterfactual fairness
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva · 2017
Earlier work this paper cites.
Opening the black box of deep neural networks via information
Ravid Shwartz-Ziv and Naftali Tishby · 2017
Cited alongside, same era.
Turning a blind eye: Explicit removal of biases and variation from deep neural network embeddings
Mohsan Alvi, Andrew Zisserman, and Christoffer Nellåker · 2018
Cited alongside, same era.
Mutual information neural estimation
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and Devon Hjelm · 2018
Cited alongside, same era.
Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
Cited alongside, same era.
The measure and mismeasure of fairness: A critical review of fair machine learning
Sam Corbett-Davies and Sharad Goel · 2018
Cited alongside, same era.
Representation learning with statistical independence to mitigate bias
Ehsan Adeli, Qingyu Zhao, Adolf Pfefferbaum, Edith V Sullivan, Li Fei-Fei, Juan Carlos Niebles, and Kilian M Pohl · 2021
Later among the works it cites.
Towards causal benchmarking of biasin face analysis algorithms
Guha Balakrishnan, Yuanjun Xiong, Wei Xia, and Pietro Perona · 2021
Later among the works it cites.
Unbiased classification through bias-contrastive and bias-balanced learning
Youngkyu Hong and Eunho Yang · 2021
Later among the works it cites.
Fairface: Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation
Kimmo Karkkainen and Jungseock Joo · 2021
Later among the works it cites.
Impossibility results for fair representations
Tosca Lechner, Shai Ben-David, Sushant Agarwal, and Nivasini Ananthakrishnan · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2018
Cited alongside, same era.
Fairness under unawareness: Assessing disparity when protected class is unobserved
Jiahao Chen, Nathan Kallus, Xiaojie Mao, Geoffry Svacha, and Madeleine Udell · 2019
Cited alongside, same era.
Learning not to learn: Training deep neural networks with biased data
Byungju Kim, Hyunwoo Kim, Kyungsu Kim, Sungjin Kim, and Junmo Kim · 2019
Cited alongside, same era.
Racial faces in the wild: Reducing racial bias by information maximization adaptation network
Mei Wang, Weihong Deng, Jiani Hu, Xunqiang Tao, and Yaohai Huang · 2019
Cited alongside, same era.
Is there a trade-off between fairness and accuracy? a perspective using mismatched hypothesis testing
Sanghamitra Dutta, Dennis Wei, Hazar Yueksel, Pin-Yu Chen, Sijia Liu, and Kush Varshney · 2020
Cited alongside, same era.
Learning meta face recognition in unseen domains
Jianzhu Guo, Xiangyu Zhu, Chenxu Zhao, Dong Cao, Zhen Lei, and Stan Z Li · 2020
Cited alongside, same era.
Learning from failure: De-biasing classifier from biased classifier
Junhyun Nam, Hyuntak Cha, Sungsoo Ahn, Jaeho Lee, and Jinwoo Shin · 2020
Cited alongside, same era.
Information-theoretic bias assessment of learned representations of pretrained face recognition
Jiazhi Li and Wael Abd-Almageed · 2021
Later among the works it cites.
Learning unbiased representations via mutual information backpropagation
Ruggero Ragonesi, Riccardo Volpi, Jacopo Cavazza, and Vittorio Murino · 2021
Later among the works it cites.
End: Entangling and disentangling deep representations for bias correction
Enzo Tartaglione, Carlo Alberto Barbano, and Marco Grangetto · 2021
Later among the works it cites.
Learning bias-invariant representation by cross-sample mutual information minimization
Wei Zhu, Haitian Zheng, Haofu Liao, Weijian Li, and Jiebo Luo · 2021
Later among the works it cites.
Epistemic uncertainty-weighted loss for visual bias mitigation
Rebecca S Stone, Nishant Ravikumar, Andrew J Bulpitt, and David C Hogg · 2022
Later among the works it cites.
Inherent tradeoffs in learning fair representations
Han Zhao and Geoffrey J Gordon · 2022
Later among the works it cites.
Cat: Controllable attribute translation for fair facial attribute classification
Jiazhi Li and Wael Abd-Almageed · 2023
Closest in time.
Ethics and fairness for diabetes artificial intelligence
Jiazhi Li and Wael AbdAlmageed · 2024
Closest in time.