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Despite a surge of recent advances in promoting machine Learning (ML) fairness, the existing mainstream approaches mostly require retraining or finetuning the entire weights of the neural network to meet the fairness criteria.
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Arthur Asuncion and David Newman, · 2007
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“Three naive bayes approaches for discrimination-free classification,”
Toon Calders and Sicco Verwer, · 2010
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“Data preprocessing techniques for classification without discrimination,”
Faisal Kamiran and Toon Calders, · 2011
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“Fairness-aware classifier with prejudice remover regularizer,”
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma, · 2012
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Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard S. Zemel, · 2012
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“Decision theory for discrimination-aware classification,”
Faisal Kamiran, Asim Karim, and Xiangliang Zhang, · 2012
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“Learning fair representations,”
Richard S. Zemel, Ledell Yu Wu, Kevin Swersky, Toniann Pitassi, and Cynthia Dwork, · 2013
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“Estimating or propagating gradients through stochastic neurons for conditional computation,”
Yoshua Bengio, Nicholas Léonard, and Aaron C. Courville, · 2013
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“Certifying and removing disparate impact,”
Michael Feldman, Sorelle A. Friedler, John Moeller, Carlos Eduardo Scheidegger, and Suresh Venkatasubramanian, · 2015
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“Deep learning face attributes in the wild,”
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang, · 2015
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“The variational fair autoencoder,”
Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard Zemel, · 2015
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“Equality of opportunity in supervised learning,”
Moritz Hardt, Eric Price, and Nathan Srebro, · 2016
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“Deep residual learning for image recognition,”
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, · 2016
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“Fairness constraints: Mechanisms for fair classification,”
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, and Krishna P. Gummadi, · 2017
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“Fair kernel learning,”
Adrián Pérez-Suay, Valero Laparra, Gonzalo Mateo-García, Jordi Muñoz-Marí, Luis Gómez-Chova, and Gustau Camps-Valls, · 2017
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“Optimized pre-processing for discrimination prevention,”
Flávio du Pin Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R. Varshney, · 2017
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“Learning non-discriminatory predictors,”
Blake E. Woodworth, Suriya Gunasekar, Mesrob I. Ohannessian, and Nathan Srebro, · 2017
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“Men also like shopping: Reducing gender bias amplification using corpus-level constraints,”
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang, · 2017
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“Decoupled weight decay regularization,”
Ilya Loshchilov and Frank Hutter, · 2017
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“Grad-cam: Visual explanations from deep networks via gradient-based localization,”
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra, · 2017
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“Axiomatic attribution for deep networks,”
Mukund Sundararajan, Ankur Taly, and Qiqi Yan, · 2017
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“On fairness and calibration,”
Geoff Pleiss, M. Raghavan, Felix Wu, J. Kleinberg, and Kilian Q. Weinberger, · 2017
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“Measuring and mitigating unintended bias in text classification,”
Lucas Dixon, John Li, Jeffrey Scott Sorensen, Nithum Thain, and Lucy Vasserman, · 2018
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“Reducing gender bias in abusive language detection,”
J. Park, Jamin Shin, and Pascale Fung, · 2018
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“A reductions approach to fair classification,”
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna M. Wallach, · 2018
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“Adversarial reprogramming of neural networks,”
Gamaleldin F Elsayed, Ian Goodfellow, and Jascha Sohl-Dickstein, · 2018
Cited alongside, same era.
“The frontiers of fairness in machine learning,”
A. Chouldechova and Aaron Roth, · 2018
Cited alongside, same era.
“Fairness without demographics in repeated loss minimization,”
T. Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang, · 2018
Cited alongside, same era.
“Mitigating unwanted biases with adversarial learning,”
B. Zhang, Blake Lemoine, and Margaret Mitchell, · 2018
Cited alongside, same era.
“Decoupled classifiers for group-fair and efficient machine learning,”
Cynthia Dwork, Nicole Immorlica, Adam Tauman Kalai, and Mark D. M. Leiserson, · 2018
Cited alongside, same era.
“Equalized odds postprocessing under imperfect group information,”
Pranjal Awasthi, Matthäus Kleindessner, and Jamie H. Morgenstern, · 2020
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“Eliciting knowledge from language models using automatically generated prompts,”
Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh, · 2020
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“Investigating bias and fairness in facial expression recognition,”
Tian Xu, Jennifer White, Sinan Kalkan, and Hatice Gunes, · 2020
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“Counterfactual generation and fairness evaluation using adversarially learned inference,”
Saloni Dash and Amit Sharma, · 2020
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“Fairfacegan: Fairness-aware facial image-to-image translation,”
Sunhee Hwang, Sungho Park, Dohyung Kim, Mirae Do, and Hyeran Byun, · 2020
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Paarth Neekhara, Shehzeen Hussain, Shlomo Dubnov, and Farinaz Koushanfar, · 2018
Cited alongside, same era.
“Fairgan: Fairness-aware generative adversarial networks,”
Depeng Xu, Shuhan Yuan, Lu Zhang, and Xintao Wu, · 2018
Cited alongside, same era.
“Multi-label learning based deep transfer neural network for facial attribute classification,”
Ni Zhuang, Yan Yan, Si Chen, Hanzi Wang, and Chunhua Shen, · 2018
Cited alongside, same era.
“AI Fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias,” Oct. 2018
Rachel K. E. Bellamy, Kuntal Dey, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, Seema Nagar, Karthikeyan Natesan Ramamurthy, John Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, and Yunfeng Zhang, · 2018
Cited alongside, same era.
“Rényi fair inference,”
Sina Baharlouei, Maher Nouiehed, and Meisam Razaviyayn, · 2019
Cited alongside, same era.
“Improving fairness in machine learning systems: What do industry practitioners need?,”
Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé, Miroslav Dudík, and H. Wallach, · 2019
Cited alongside, same era.
“Mitigating gender bias in natural language processing: Literature review,”
Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth M. Belding-Royer, Kai-Wei Chang, and William Yang Wang, · 2019
Cited alongside, same era.
“Captum: A unified and generic model interpretability library for pytorch,” 2020
Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Bilal Alsallakh, Jonathan Reynolds, Alexander Melnikov, Natalia Kliushkina, Carlos Araya, Siqi Yan, and Orion Reblitz-Richardson, · 2020
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“Warp: Word-level adversarial reprogramming,”
Karen Hambardzumyan, Hrant Khachatrian, and Jonathan May, · 2021
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Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig, · 2021
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“A survey on bias and fairness in machine learning,”
Ninareh Mehrabi, Fred Morstatter, Nripsuta Ani Saxena, Kristina Lerman, and A. G. Galstyan, · 2021
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“A survey of race, racism, and anti-racism in nlp,”
Anjalie Field, Su Lin Blodgett, Zeerak Waseem, and Yulia Tsvetkov, · 2021
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“Group fairness: Independence revisited,”
Tim Räz, · 2021
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“Post-processing for individual fairness,”
Felix Petersen, Debarghya Mukherjee, Yuekai Sun, and Mikhail Yurochkin, · 2021
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“Fairness in risk assessment instruments: Post-processing to achieve counterfactual equalized odds,”
Alan Mishler and Edward H. Kennedy, · 2021
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“Priority-based post-processing bias mitigation for individual and group fairness,”
Pranay Kr. Lohia, · 2021
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“Why adversarial reprogramming works, when it fails, and how to tell the difference,”
Yang Zheng, Xiaoyi Feng, Zhaoqiang Xia, Xiaoyue Jiang, Ambra Demontis, Maura Pintor, Battista Biggio, and Fabio Roli, · 2021
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“Making pre-trained language models better few-shot learners,”
Tianyu Gao, Adam Fisch, and Danqi Chen, · 2021
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“Prefix-tuning: Optimizing continuous prompts for generation,”
Xiang Lisa Li and Percy Liang, · 2021
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“It’s not just size that matters: Small language models are also few-shot learners,”
Timo Schick and Hinrich Schütze, · 2021
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“Wilds: A benchmark of in-the-wild distribution shifts,”
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Wei hua Hu, Michihiro Yasunaga, Richard L. Phillips, Sara Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang, · 2021
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“Openprompt: An open-source framework for prompt-learning,”
Ning Ding, Shengding Hu, Weilin Zhao, Yulin Chen, Zhiyuan Liu, Haitao Zheng, and Maosong Sun, · 2022
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“Visual prompting: Modifying pixel space to adapt pre-trained models,”
Hyojin Bahng, Ali Jahanian, Swami Sankaranarayanan, and Phillip Isola, · 2022
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“Cross-modal adversarial reprogramming,”
Paarth Neekhara, Shehzeen Hussain, Jinglong Du, Shlomo Dubnov, Farinaz Koushanfar, and Julian McAuley, · 2022
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“Reprogramming fairgans with variational auto-encoders: A new transfer learning model,”
Beatrice Nobile, Gabriele Santin, Bruno Lepri, and Pierpaolo Brutti, · 2022
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“Black-box prompt learning for pre-trained language models,”
Shizhe Diao, Xuechun Li, Yong Lin, Zhichao Huang, and Tong Zhang, · 2022
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“How to robustify black-box ml models? a zeroth-order optimization perspective,”
Yimeng Zhang, Yuguang Yao, Jinghan Jia, Jinfeng Yi, Mingyi Hong, Shiyu Chang, and Sijia Liu, · 2022
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