Fetching the paper…
Reading the bibliography…
In recent years, the rapid advancement of machine learning (ML) models, particularly transformer-based pre-trained models, has revolutionized Natural Language Processing (NLP) and Computer Vision (CV) fields.
Intrinsic Bias Metrics Do Not Correlate with Application Bias. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) . 1926–1940
Seraphina Goldfarb-Tarrant, Rebecca Marchant, Ricardo Muñoz Sánchez, Mugdha Pandya, and Adam Lopez. 2021 · 1940
Earlier work this paper cites.
Unifying Vision-and-Language Tasks via Text Generation. In Proceedings of the 38th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 139) , Marina Meila and Tong Zhang (Eds.). PMLR, 1931–1942
Jaemin Cho, Jie Lei, Hao Tan, and Mohit Bansal. 2021 · 1942
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
Earlier work this paper cites.
CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) . 1953–1967
Nikita Nangia, Clara Vania, Rasika Bhalerao, and Samuel Bowman. 2020 · 1967
Earlier work this paper cites.
Soleil et peau
Thomas B Fitzpatrick. 1975 · 1975
Earlier work this paper cites.
Measuring individual differences in implicit cognition: the implicit association test
Anthony G Greenwald, Debbie E McGhee, and Jordan LK Schwartz. 1998 · 1998
Earlier work this paper cites.
Gender Bias in Coreference Resolution. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) . Association for Computational Linguistics, New Orleans, Louisiana, 8–14
Rachel Rudinger, Jason Naradowsky, Brian Leonard, and Benjamin Van Durme. 2018 · 2002
Earlier work this paper cites.
Cost-sensitive learning vs. sampling: Which is best for handling unbalanced classes with unequal error costs?
Gary M Weiss, Kate McCarthy, and Bibi Zabar. 2007 · 2007
Earlier work this paper cites.
Are Gender-Neutral Queries Really Gender-Neutral? Mitigating Gender Bias in Image Search. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing . 1995–2008
Jialu Wang, Yang Liu, and Xin Wang. 2021 · 2008
Earlier work this paper cites.
Principal component analysis
Hervé Abdi and Lynne J. Williams. 2010 · 2010
Earlier work this paper cites.
Fairness through awareness. In Proceedings of the 3rd innovations in theoretical computer science conference . 214–226
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. 2012 · 2012
Earlier work this paper cites.
Learning fair representations. In International conference on machine learning . PMLR, 325–333
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork. 2013 · 2013
Earlier work this paper cites.
Mechanisms of linguistic bias: How words reflect and maintain stereotypic expectancies
Camiel J Beukeboom, J Forgas, O Vincze, and J Laszlo. 2014 · 2014
Earlier work this paper cites.
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. 2014 · 2014
Earlier work this paper cites.
Skin tone stratification among Black Americans, 2001–2003
Ellis P Monk Jr. 2014 · 2014
Earlier work this paper cites.
From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaier. 2014 · 2014
Earlier work this paper cites.
Censoring representations with an adversary
Harrison Edwards and Amos Storkey. 2015 · 2015
Earlier work this paper cites.
Unequal representation and gender stereotypes in image search results for occupations. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems . ACM, 3819–3828
Matthew Kay, Cynthia Matuszek, and Sean A Munson. 2015 · 2015
Earlier work this paper cites.
Man is to computer programmer as woman is to homemaker? debiasing word embeddings. In Advances in Neural Information Processing Systems . 4349–4357
Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai. 2016 · 2016
Earlier work this paper cites.
A confidence-based approach for balancing fairness and accuracy. In Proceedings of the 2016 SIAM international conference on data mining . SIAM, 144–152
Benjamin Fish, Jeremy Kun, and Ádám D Lelkes. 2016 · 2016
Earlier work this paper cites.
Equality of opportunity in supervised learning. In Advances in neural information processing systems . 3315–3323
Moritz Hardt, Eric Price, Nati Srebro, et al · 2016
Earlier work this paper cites.
The multiple dimensions of race
Wendy D. Roth. 2016 · 2016
Earlier work this paper cites.
Stereotyping and bias in the flickr30k dataset
Emiel Van Miltenburg. 2016 · 2016
Earlier work this paper cites.
The problem with bias: Allocative versus representational harms in machine learning. In 9th Annual conference of the special interest group for computing, information and society
Solon Barocas, Kate Crawford, Aaron Shapiro, and Hanna Wallach. 2017 · 2017
Earlier work this paper cites.
Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J Bryson, and Arvind Narayanan. 2017 · 2017
Earlier work this paper cites.
Measuring and Mitigating Unintended Bias in Text Classification. In AAAI
Lucas Dixon, John Li, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman. 2017 · 2017
Earlier work this paper cites.
Mask R-CNN. In 2017 IEEE International Conference on Computer Vision (ICCV) . 2980–2988
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. 2017 · 2017
Earlier work this paper cites.
Counterfactual fairness
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva. 2017 · 2017
Earlier work this paper cites.
On fairness and calibration
Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q Weinberger. 2017 · 2017
Earlier work this paper cites.
Inclusivefacenet: Improving face attribute detection with race and gender diversity
Hee Jung Ryu, Hartwig Adam, and Margaret Mitchell. 2017 · 2017
Earlier work this paper cites.
Shreya Shankar, Yoni Halpern, Eric Breck, James Atwood, Jimbo Wilson, and D Sculley. 2017 · 2017
Earlier work this paper cites.
Age progression/regression by conditional adversarial autoencoder. In Proceedings of the IEEE conference on computer vision and pattern recognition . 5810–5818
Zhifei Zhang, Yang Song, and Hairong Qi. 2017 · 2017
Earlier work this paper cites.
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 · 2017
Earlier work this paper cites.
Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings. In Computer Vision – ECCV 2018 Workshops: Munich, Germany, September 8-14, 2018, Proceedings, Part I (Munich, Germany). Springer-Verlag, Berlin, Heidelberg, 556–572
Mohsan Alvi, Andrew Zisserman, and Christoffer Nellåker. 2019 · 2018
Earlier work this paper cites.
Convolutional Image Captioning. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE Computer Society, Los Alamitos, CA, USA, 5561–5570
J. Aneja, A. Deshpande, and A. G. Schwing. 2018 · 2018
Earlier work this paper cites.
Relational inductive biases, deep learning, and graph networks
Peter Battaglia, Jessica Blake Chandler Hamrick, Victor Bapst, Alvaro Sanchez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Caglar Gulcehre, Francis Song, Andy Ballard, Justin Gilmer, George E. Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Jayne Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matt Botvinick, Oriol Vinyals, Yujia Li, and Razvan Pascanu. 2018 · 2018
Earlier work this paper cites.
Fairness in machine learning: Lessons from political philosophy. In Conference on fairness, accountability and transparency . PMLR, 149–159
Reuben Binns. 2018 · 2018
Earlier work this paper cites.
Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency . PMLR, 77–91
Joy Buolamwini and Timnit Gebru. 2018 · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Earlier work this paper cites.
Imbalanced deep learning by minority class incremental rectification
Qi Dong, Shaogang Gong, and Xiatian Zhu. 2018 · 2018
Earlier work this paper cites.
Decoupled classifiers for group-fair and efficient machine learning. In Conference on fairness, accountability and transparency . PMLR, 119–133
Cynthia Dwork, Nicole Immorlica, Adam Tauman Kalai, and Max Leiserson. 2018 · 2018
Earlier work this paper cites.
Adversarial Removal of Demographic Attributes from Text Data
Yanai Elazar and Yoav Goldberg. 2018 · 2018
Earlier work this paper cites.
Women also snowboard: Overcoming bias in captioning models. In Proceedings of the European conference on computer vision (ECCV) . 771–787
Lisa Anne Hendricks, Kaylee Burns, Kate Saenko, Trevor Darrell, and Anna Rohrbach. 2018 · 2018
Earlier work this paper cites.
Preventing fairness gerrymandering: Auditing and learning for subgroup fairness. In International conference on machine learning . PMLR, 2564–2572
Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu. 2018 · 2018
Earlier work this paper cites.
Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems. In Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics . 43–53
Svetlana Kiritchenko and Saif Mohammad. 2018 · 2018
Earlier work this paper cites.
Towards Robust and Privacy-preserving Text Representations
Yitong Li, Timothy Baldwin, and Trevor Cohn. 2018 · 2018
Earlier work this paper cites.
Learning adversarially fair and transferable representations. In International Conference on Machine Learning . PMLR, 3384–3393
David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel. 2018 · 2018
Earlier work this paper cites.
Invariant representations without adversarial training
Daniel Moyer, Shuyang Gao, Rob Brekelmans, Aram Galstyan, and Greg Ver Steeg. 2018 · 2018
Earlier work this paper cites.
Understanding unequal gender classification accuracy from face images
Vidya Muthukumar, Tejaswini Pedapati, Nalini Ratha, Prasanna Sattigeri, Chai-Wah Wu, Brian Kingsbury, Abhishek Kumar, Samuel Thomas, Aleksandra Mojsilovic, and Kush R Varshney. 2018 · 2018
Earlier work this paper cites.
Reducing Gender Bias in Abusive Language Detection. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, Brussels, Belgium, 2799–2804
Ji Ho Park, Jamin Shin, and Pascale Fung. 2018 · 2018
Earlier work this paper cites.
Racial influence on automated perceptions of emotions
Lauren Rhue. 2018 · 2018
Earlier work this paper cites.
Algorithms of oppression: how search engines reinforce racism: by Safiya Umoja Noble, New York, USA, NYU Press, 2018, xv+ 229pp.,£ 22.99 (paperback), ISBN 9781479837243
Sanjay Sharma. 2020 · 2018
Earlier work this paper cites.
Getting Gender Right in Neural Machine Translation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . 3003–3008
Eva Vanmassenhove, Christian Hardmeier, and Andy Way. 2018 · 2018
Earlier work this paper cites.
Mind the GAP: A balanced corpus of gendered ambiguous pronouns
Kellie Webster, Marta Recasens, Vera Axelrod, and Jason Baldridge. 2018 · 2018
Earlier work this paper cites.
Fairgan: Fairness-aware generative adversarial networks. In 2018 IEEE International Conference on Big Data (Big Data) . IEEE, 570–575
Depeng Xu, Shuhan Yuan, Lu Zhang, and Xintao Wu. 2018 · 2018
Earlier work this paper cites.
Mitigating unwanted biases with adversarial learning
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell. 2018 · 2018
Earlier work this paper cites.
Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) . 15–20
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2018a · 2018
Earlier work this paper cites.
Learning Gender-Neutral Word Embeddings. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, Brussels, Belgium, 4847–4853
Jieyu Zhao, Yichao Zhou, Zeyu Li, Wei Wang, and Kai-Wei Chang. 2018b · 2018
Earlier work this paper cites.
Exposing and correcting the gender bias in image captioning datasets and models
Shruti Bhargava and David Forsyth. 2019 · 2019
Earlier work this paper cites.
Rubi: Reducing unimodal biases for visual question answering
Remi Cadene, Corentin Dancette, Matthieu Cord, Devi Parikh, et al · 2019
Earlier work this paper cites.
Cross-Lingual Language Model Pretraining
Alexis Conneau and Guillaume Lample. 2019 · 2019
Earlier work this paper cites.
Flexibly fair representation learning by disentanglement. In International conference on machine learning . PMLR, 1436–1445
Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa Weis, Kevin Swersky, Toniann Pitassi, and Richard Zemel. 2019 · 2019
Earlier work this paper cites.
Does object recognition work for everyone?. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops . 52–59
Terrance De Vries, Ishan Misra, Changhan Wang, and Laurens Van der Maaten. 2019 · 2019
Earlier work this paper cites.
Image counterfactual sensitivity analysis for detecting unintended bias
Emily Denton, Ben Hutchinson, Margaret Mitchell, Timnit Gebru, and Andrew Zaldivar. 2019 · 2019
Cited alongside, same era.
Unified Language Model Pre-training for Natural Language Understanding and Generation. In Advances in Neural Information Processing Systems , H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett (Eds.), Vol. 32. Curran Associates, Inc
Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019 · 2019
Cited alongside, same era.
Fairness-aware ranking in search & recommendation systems with application to linkedin talent search. In Proceedings of the 25th acm sigkdd international conference on knowledge discovery & data mining . 2221–2231
Sahin Cem Geyik, Stuart Ambler, and Krishnaram Kenthapadi. 2019 · 2019
Cited alongside, same era.
Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) . 609–614
Bias and fairness in multimodal machine learning: A case study of automated video interviews. In Proceedings of the 2021 International Conference on Multimodal Interaction . 268–277
Brandon M Booth, Louis Hickman, Shree Krishna Subburaj, Louis Tay, Sang Eun Woo, and Sidney K D’Mello. 2021 · 2021
Later among the works it cites.
IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, 8875–8898
Samuel Cahyawijaya, Genta Indra Winata, Bryan Wilie, Karissa Vincentio, Xiaohong Li, Adhiguna Kuncoro, Sebastian Ruder, Zhi Yuan Lim, Syafri Bahar, Masayu Khodra, Ayu Purwarianti, and Pascale Fung. 2021 · 2021
Later among the works it cites.
FairFil: Contrastive Neural Debiasing Method for Pretrained Text Encoders. In International Conference on Learning Representations
Pengyu Cheng, Weituo Hao, Siyang Yuan, Shijing Si, and Lawrence Carin. 2021 · 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…
Hila Gonen and Yoav Goldberg. 2019 · 2019
Cited alongside, same era.
Bias correction of learned generative models using likelihood-free importance weighting
Aditya Grover, Jiaming Song, Ashish Kapoor, Kenneth Tran, Alekh Agarwal, Eric J Horvitz, and Stefano Ermon. 2019 · 2019
Cited alongside, same era.
Deep imbalanced learning for face recognition and attribute prediction
Chen Huang, Yining Li, Chen Change Loy, and Xiaoou Tang. 2019 · 2019
Cited alongside, same era.
The global landscape of AI ethics guidelines
Anna Jobin, Marcello Ienca, and Effy Vayena. 2019 · 2019
Cited alongside, same era.
Gender-preserving Debiasing for Pre-trained Word Embeddings. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics . Association for Computational Linguistics, Florence, Italy, 1641–1650
Masahiro Kaneko and Danushka Bollegala. 2019 · 2019
Cited alongside, same era.
An empirical study of rich subgroup fairness for machine learning. In Proceedings of the conference on fairness, accountability, and transparency . 100–109
Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu. 2019 · 2019
Cited alongside, same era.
Striking the right balance with uncertainty. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 103–112
Salman Khan, Munawar Hayat, Syed Waqas Zamir, Jianbing Shen, and Ling Shao. 2019 · 2019
Cited alongside, same era.
Multiaccuracy: Black-box post-processing for fairness in classification. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society . 247–254
Michael P Kim, Amirata Ghorbani, and James Zou. 2019 · 2019
Cited alongside, same era.
Exploring Social Bias in Chatbots using Stereotype Knowledge. In Proceedings of the 2019 Workshop on Widening NLP . 177–180
Nayeon Lee, Andrea Madotto, and Pascale Fung. 2019 · 2019
Cited alongside, same era.
Ching-Yao Chuang and Youssef Mroueh. 2021 · 2021
Later among the works it cites.
Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics
Paula Czarnowska, Yogarshi Vyas, and Kashif Shah. 2021 · 2021
Later among the works it cites.
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In International Conference on Learning Representations
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2021 · 2021
Later among the works it cites.
An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations
Luciano Floridi, Josh Cowls, Monica Beltrametti, Raja Chatila, Patrice Chazerand, Virginia Dignum, Christoph Luetge, Robert Madelin, Ugo Pagallo, Francesca Rossi, et al · 2021
Later among the works it cites.
A survey on bias in deep NLP
Ismael Garrido-Muñoz, Arturo Montejo-Ráez, Fernando Martínez-Santiago, and L Alfonso Ureña-López. 2021 · 2021
Later among the works it cites.
Mitigating face recognition bias via group adaptive classifier. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 3414–3424
Sixue Gong, Xiaoming Liu, and Anil K Jain. 2021 · 2021
Later among the works it cites.
Detecting emergent intersectional biases: Contextualized word embeddings contain a distribution of human-like biases. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society . 122–133
Wei Guo and Aylin Caliskan. 2021 · 2021
Later among the works it cites.
Towards measuring fairness in ai: the casual conversations dataset
Caner Hazirbas, Joanna Bitton, Brian Dolhansky, Jacqueline Pan, Albert Gordo, and Cristian Canton Ferrer. 2021 · 2021
Later among the works it cites.
Masked Autoencoders Are Scalable Vision Learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Doll’ar, and Ross B. Girshick. 2021 · 2021
Later among the works it cites.
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision. In Proceedings of the 38th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 139) , Marina Meila and Tong Zhang (Eds.). PMLR, 4904–4916
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. 2021 · 2021
Later among the works it cites.
Fairface: Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision . 1548–1558
Kimmo Karkkainen and Jungseock Joo. 2021 · 2021
Later among the works it cites.
Age bias in emotion detection: An analysis of facial emotion recognition performance on young, middle-aged, and older adults. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society . 638–644
Eugenia Kim, De’Aira Bryant, Deepak Srikanth, and Ayanna Howard. 2021a · 2021
Later among the works it cites.
Align before fuse: Vision and language representation learning with momentum distillation
Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. 2021 · 2021
Later among the works it cites.
M6: Multi-Modality-to-Multi-Modality Multitask Mega-Transformer for Unified Pretraining. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (Virtual Event, Singapore) (KDD ’21) . Association for Computing Machinery, New York, NY, USA, 3251–3261
Junyang Lin, Rui Men, An Yang, Chang Zhou, Yichang Zhang, Peng Wang, Jingren Zhou, Jie Tang, and Hongxia Yang. 2021 · 2021
Later among the works it cites.
A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. 2021 · 2021
Later among the works it cites.
StereoSet: Measuring stereotypical bias in pretrained language models. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) . 5356–5371
Moin Nadeem, Anna Bethke, and Siva Reddy. 2021 · 2021
Later among the works it cites.
Equity in skin typing: why it is time to replace the Fitzpatrick scale
UK Okoji, SC Taylor, and JB Lipoff. 2021 · 2021
Later among the works it cites.
Counterfactual Inference for Text Classification Debiasing. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) . Association for Computational Linguistics, Online, 5434–5445
Chen Qian, Fuli Feng, Lijie Wen, Chunping Ma, and Pengjun Xie. 2021 · 2021
Later among the works it cites.
Learning Transferable Visual Models From Natural Language Supervision. In Proceedings of the 38th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 139) , Marina Meila and Tong Zhang (Eds.). PMLR, 8748–8763
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021 · 2021
Later among the works it cites.
Fair attribute classification through latent space de-biasing. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 9301–9310
Vikram V Ramaswamy, Sunnie SY Kim, and Olga Russakovsky. 2021 · 2021
Later among the works it cites.
Zero-shot text-to-image generation. In International Conference on Machine Learning . PMLR, 8821–8831
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021 · 2021
Later among the works it cites.
minDALL-E on Conceptual Captions
Chiheon Kim Doyup Lee Saehoon Kim, Sanghun Cho and Woonhyuk Baek. 2021 · 2021
Later among the works it cites.
Gender bias in machine translation
Beatrice Savoldi, Marco Gaido, Luisa Bentivogli, Matteo Negri, and Marco Turchi. 2021 · 2021
Later among the works it cites.
Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP
Timo Schick, Sahana Udupa, and Hinrich Schütze. 2021 · 2021
Later among the works it cites.
A step toward more inclusive people annotations for fairness. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society . 916–925
Candice Schumann, Susanna Ricco, Utsav Prabhu, Vittorio Ferrari, and Caroline Pantofaru. 2021 · 2021
Later among the works it cites.
Revealing persona biases in dialogue systems
Emily Sheng, Josh Arnold, Zhou Yu, Kai-Wei Chang, and Nanyun Peng. 2021a · 2021
Later among the works it cites.
Worst of both worlds: Biases compound in pre-trained vision-and-language models
Tejas Srinivasan and Yonatan Bisk. 2021 · 2021
Later among the works it cites.
Image representations learned with unsupervised pre-training contain human-like biases. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency . 701–713
Ryan Steed and Aylin Caliskan. 2021 · 2021
Later among the works it cites.
Measuring fairness in generative models
Christopher TH Teo and Ngai-Man Cheung. 2021 · 2021
Later among the works it cites.
White House guidance for regulation of Artificial Intelligence Applications
Russell T Vought. 2021 · 2021
Later among the works it cites.
VinVL: Making Visual Representations Matter in Vision-Language Models
Pengchuan Zhang, Xiujun Li, Xiaowei Hu, Jianwei Yang, Lei Zhang, Lijuan Wang, Yejin Choi, and Jianfeng Gao. 2021 · 2021
Later among the works it cites.
On Measuring Social Biases in Prompt-Based Multi-Task Learning. In Findings of the Association for Computational Linguistics: NAACL 2022 . Association for Computational Linguistics, Seattle, United States, 551–564
Afra Feyza Akyürek, Sejin Paik, Muhammed Kocyigit, Seda Akbiyik, Serife Leman Runyun, and Derry Wijaya. 2022 · 2022
Later among the works it cites.
A prompt array keeps the bias away: Debiasing vision-language models with adversarial learning
Hugo Berg, Siobhan Mackenzie Hall, Yash Bhalgat, Wonsuk Yang, Hannah Rose Kirk, Aleksandar Shtedritski, and Max Bain. 2022 · 2022
Later among the works it cites.
Dall-eval: Probing the reasoning skills and social biases of text-to-image generative transformers
Jaemin Cho, Abhay Zala, and Mohit Bansal. 2022 · 2022
Later among the works it cites.
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation. In Findings of the Association for Computational Linguistics: ACL 2022 . Association for Computational Linguistics, Dublin, Ireland, 2383–2395
Wenliang Dai, Lu Hou, Lifeng Shang, Xin Jiang, Qun Liu, and Pascale Fung. 2022 · 2022
Later among the works it cites.
Measuring Fairness with Biased Rulers: A Comparative Study on Bias Metrics for Pre-trained Language Models. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . 1693–1706
Pieter Delobelle, Ewoenam Tokpo, Toon Calders, and Bettina Berendt. 2022 · 2022
Later among the works it cites.
Inductive biases for deep learning of higher-level cognition
Anirudh Goyal and Yoshua Bengio. 2022 · 2022
Later among the works it cites.
Fairness indicators for systematic assessments of visual feature extractors. In 2022 ACM Conference on Fairness, Accountability, and Transparency . 70–88
Priya Goyal, Adriana Romero Soriano, Caner Hazirbas, Levent Sagun, and Nicolas Usunier. 2022 · 2022
Later among the works it cites.
Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal. In Findings of the Association for Computational Linguistics: ACL 2022 . Association for Computational Linguistics, Dublin, Ireland, 658–678
Umang Gupta, Jwala Dhamala, Varun Kumar, Apurv Verma, Yada Pruksachatkun, Satyapriya Krishna, Rahul Gupta, Kai-Wei Chang, Greg Ver Steeg, and Aram Galstyan. 2022 · 2022
Later among the works it cites.
Caner Hazirbas, Yejin Bang, Tiezheng Yu, Parisa Assar, Bilal Porgali, Vítor Albiero, Stefan Hermanek, Jacqueline Pan, Emily McReynolds, Miranda Bogen, et al · 2022
Later among the works it cites.
Scaling Up Vision-Language Pre-Training for Image Captioning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . 17980–17989
Xiaowei Hu, Zhe Gan, Jianfeng Wang, Zhengyuan Yang, Zicheng Liu, Yumao Lu, and Lijuan Wang. 2022 · 2022
Later among the works it cites.
Scaling Language-Image Pre-training via Masking
Yanghao Li, Haoqi Fan, Ronghang Hu, Christoph Feichtenhofer, and Kaiming He. 2022a · 2022
Later among the works it cites.
An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 1878–1898
Nicholas Meade, Elinor Poole-Dayan, and Siva Reddy. 2022 · 2022
Later among the works it cites.
French CrowS-Pairs: Extending a challenge dataset for measuring social bias in masked language models to a language other than English. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Association for Computational Linguistics, Dublin, Ireland, 8521–8531
Aurélie Névéol, Yoann Dupont, Julien Bezançon, and Karën Fort. 2022 · 2022
Later among the works it cites.
The Dollar Street Dataset: Images Representing the Geographic and Socioeconomic Diversity of the World. In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track
William A Gaviria Rojas, Sudnya Diamos, Keertan Ranjan Kini, David Kanter, Vijay Janapa Reddi, and Cody Coleman. 2022 · 2022
Later among the works it cites.
High-Resolution Image Synthesis With Latent Diffusion Models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . 10684–10695
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022 · 2022
Later among the works it cites.
Understanding contrastive learning requires incorporating inductive biases. In International Conference on Machine Learning . PMLR, 19250–19286
Nikunj Saunshi, Jordan Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham Kakade, and Akshay Krishnamurthy. 2022 · 2022
Later among the works it cites.
How Much Can CLIP Benefit Vision-and-Language Tasks?. In International Conference on Learning Representations
Sheng Shen, Liunian Harold Li, Hao Tan, Mohit Bansal, Anna Rohrbach, Kai-Wei Chang, Zhewei Yao, and Kurt Keutzer. 2022 · 2022
Later among the works it cites.
SimVLM: Simple Visual Language Model Pretraining with Weak Supervision. In International Conference on Learning Representations
Zirui Wang, Jiahui Yu, Adams Wei Yu, Zihang Dai, Yulia Tsvetkov, and Yuan Cao. 2022 · 2022
Later among the works it cites.
American== white in multimodal language-and-image ai. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society . 800–812
Robert Wolfe and Aylin Caliskan. 2022a · 2022
Later among the works it cites.
Markedness in visual semantic ai. In 2022 ACM Conference on Fairness, Accountability, and Transparency . 1269–1279
Robert Wolfe and Aylin Caliskan. 2022b · 2022
Later among the works it cites.
FILIP: Fine-grained Interactive Language-Image Pre-Training. In International Conference on Learning Representations
Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, and Chunjing Xu. 2022 · 2022
Later among the works it cites.
LiT: Zero-Shot Transfer with Locked-image Text Tuning
Xiaohua Zhai, Xiao Wang, Basil Mustafa, Andreas Steiner, Daniel Keysers, Alexander Kolesnikov, and Lucas Beyer. 2022 · 2022
Later among the works it cites.
Counterfactually Measuring and Eliminating Social Bias in Vision-Language Pre-training Models. In Proceedings of the 30th ACM International Conference on Multimedia . 4996–5004
Yi Zhang, Junyang Wang, and Jitao Sang. 2022 · 2022
Later among the works it cites.
Debiasing vision-language models via biased prompts
Ching-Yao Chuang, Varun Jampani, Yuanzhen Li, Antonio Torralba, and Stefanie Jegelka. 2023 · 2023
Closest in time.
A Friendly Face: Do Text-to-Image Systems Rely on Stereotypes when the Input is Under-Specified?
Kathleen C Fraser, Svetlana Kiritchenko, and Isar Nejadgholi. 2023 · 2023
Closest in time.
Fair Diffusion: Instructing Text-to-Image Generation Models on Fairness
Felix Friedrich, Patrick Schramowski, Manuel Brack, Lukas Struppek, Dominik Hintersdorf, Sasha Luccioni, and Kristian Kersting. 2023 · 2023
Closest in time.
DeAR: Debiasing Vision-Language Models with Additive Residuals. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 6820–6829
Ashish Seth, Mayur Hemani, and Chirag Agarwal. 2023 · 2023
Closest in time.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop. 2023 · 2023
Closest in time.
Unified Detoxifying and Debiasing in Language Generation via Inference-time Adaptive Optimization. In The Eleventh International Conference on Learning Representations
Zonghan Yang, Xiaoyuan Yi, Peng Li, Yang Liu, and Xing Xie. 2023 · 2023
Closest in time.