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Bias amplification is a phenomenon in which models exacerbate biases or stereotypes present in the training data.
The interpretation of interaction in contingency tables
English Simpson. 1951 · 1951
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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 · 2017
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Gender bias in coreference resolution
Rachel Rudinger, Jason Naradowsky, Brian Leonard, and Benjamin Van Durme. 2018 · 2018
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Gender bias in coreference resolution: Evaluation and debiasing methods
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2018 · 2018
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Bias in bios: A case study of semantic representation bias in a high-stakes setting
Maria De-Arteaga, Alexey Romanov, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, and Adam Tauman Kalai. 2019 · 2019
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Sentence-BERT: Sentence embeddings using Siamese BERT-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
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The risk of racial bias in hate speech detection
Maarten Sap, Dallas Card, Saadia Gabriel, Yejin Choi, and Noah A. Smith. 2019 · 2019
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Balanced datasets are not enough: Estimating and mitigating gender bias in deep image representations
Tianlu Wang, Jieyu Zhao, Mark Yatskar, Kai-Wei Chang, and Vicente Ordonez. 2018 · 2019
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The pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, Shawn Presser, and Connor Leahy. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
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Multimodal datasets: misogyny, pornography, and malignant stereotypes
Abeba Birhane, Vinay Uday Prabhu, and Emmanuel Kahembwe. 2021 · 2021
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Documenting large webtext corpora: A case study on the colossal clean crawled corpus
Jesse Dodge, Maarten Sap, Ana Marasović, William Agnew, Gabriel Ilharco, Dirk Groeneveld, Margaret Mitchell, and Matt Gardner. 2021 · 2021
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Bias out-of-the-box: An empirical analysis of intersectional occupational biases in popular generative language models
Hannah Rose Kirk, Yennie Jun, Haider Iqbal, Elias Benussi, Filippo Volpin, Frédéric A. Dreyer, Aleksandar Shtedritski, and Yuki M. Asano. 2021 · 2021
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Learning transferable visual models from natural language supervision
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
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Write it like you see it: Detectable differences in clinical notes by race lead to differential model recommendations
Hammaad Adam, Ming Ying Yang, Kenrick Cato, Ioana Baldini, Charles Senteio, Leo Anthony Celi, Jiaming Zeng, Moninder Singh, and Marzyeh Ghassemi. 2022 · 2022
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How well can text-to-image generative models understand ethical natural language interventions?
Hritik Bansal, Da Yin, Masoud Monajatipoor, and Kai-Wei Chang. 2022 · 2022
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Dall-eval: Probing the reasoning skills and social biases of text-to-image generative models
Jaemin Cho, Abhay Zala, and Mohit Bansal. 2022 · 2022
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Measuring causal effects of data statistics on language model’s ‘factual’ predictions
Pythia: A suite for analyzing large language models across training and scaling
Stella Biderman, Hailey Schoelkopf, Quentin Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, Aviya Skowron, Lintang Sutawika, and Oskar van der Wal. 2023 · 2023
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Extracting training data from diffusion models
Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito, and Eric Wallace. 2023 · 2023
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Yanai Elazar, Akshita Bhagia, Ian Magnusson, Abhilasha Ravichander, Dustin Schwenk, Alane Suhr, Pete Walsh, Dirk Groeneveld, Luca Soldaini, Sameer Singh, Hanna Hajishirzi, Noah A. Smith, and Jesse Dodge. 2023 · 2023
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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
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Yanai Elazar, Nora Kassner, Shauli Ravfogel, Amir Feder, Abhilasha Ravichander, Marius Mosbach, Yonatan Belinkov, Hinrich Schütze, and Yoav Goldberg. 2022 · 2022
Cited alongside, same era.
A systematic study of bias amplification
Melissa Hall, Laurens van der Maaten, Laura Gustafson, Maxwell Jones, and Aaron Adcock. 2022 · 2022
Cited alongside, same era.
Quantifying societal bias amplification in image captioning
Y. Hirota, Y. Nakashima, and N. Garcia. 2022 · 2022
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Underspecification in scene description-to-depiction tasks
Ben Hutchinson, Jason Baldridge, and Vinodkumar Prabhakaran. 2022 · 2022
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Impact of pretraining term frequencies on few-shot numerical reasoning
Yasaman Razeghi, Robert L Logan IV, Matt Gardner, and Sameer Singh. 2022 · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022 · 2022
Cited alongside, same era.
Laion-5b: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, Patrick Schramowski, Srivatsa Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, and Jenia Jitsev. 2022 · 2022
Cited alongside, same era.
Inspecting the geographical representativeness of images from text-to-image models
Abhipsa Basu, R. Venkatesh Babu, and Danish Pruthi. 2023 · 2023
Cited alongside, same era.
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
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Uncurated image-text datasets: Shedding light on demographic bias
Noa Garcia, Yusuke Hirota, Yankun Wu, and Yuta Nakashima. 2023 · 2023
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Vision-language models performing zero-shot tasks exhibit gender-based disparities
Melissa Hall, Laura Gustafson, Aaron Adcock, Ishan Misra, and Candace Ross. 2023 · 2023
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Large language models struggle to learn long-tail knowledge
Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, and Colin Raffel. 2023 · 2023
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Shayne Longpre, Gregory Yauney, Emily Reif, Katherine Lee, Adam Roberts, Barret Zoph, Denny Zhou, Jason Wei, Kevin Robinson, David Mimno, and Daphne Ippolito. 2023 · 2023
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Stable bias: Analyzing societal representations in diffusion models
Alexandra Sasha Luccioni, Christopher Akiki, Margaret Mitchell, and Yacine Jernite. 2023 · 2023
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Resolving ambiguities in text-to-image generative models
Ninareh Mehrabi, Palash Goyal, Apurv Verma, Jwala Dhamala, Varun Kumar, Qian Hu, Kai-Wei Chang, Richard Zemel, Aram Galstyan, and Rahul Gupta. 2023 · 2023
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Social biases through the text-to-image generation lens
Ranjita Naik and Besmira Nushi. 2023 · 2023
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