Fetching the paper…
Reading the bibliography…
With rapid development and deployment of generative language models in global settings, there is an urgent need to also scale our measurements of harm, not just in the number and types of harms covered, but also how well they account for local cultural contexts, including marginalized identities and the social biases experienced by them.
Mental pictures of college girls of hindus, muslims and christians, 1965
Nataraj P · 1965
Earlier work this paper cites.
Linguistic stereotypes and social distance
Ramdas Borude · 1966
Earlier work this paper cites.
https://en.wikipedia.org/wiki/List_of_languages_by_number_of_native_speakers_in_India
List of languages by number of native speakers in india · 2010
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning · 2015
Earlier work this paper cites.
India and colorism: The finer nuances
Neha Mishra · 2015
Earlier work this paper cites.
Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai · 2016
Earlier work this paper cites.
The abc of stereotypes about groups: Agency/socioeconomic success, conservative–progressive beliefs, and communion
Alex Koch, Roland Imhoff, Ron Dotsch, Christian Unkelbach, and Hans Alves · 2016
Earlier work this paper cites.
Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan · 2017
Earlier work this paper cites.
Society-in-the-loop: programming the algorithmic social contract
Iyad Rahwan · 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
Earlier work this paper cites.
Examining gender and race bias in two hundred sentiment analysis systems, 2018
Svetlana Kiritchenko and Saif M. Mohammad · 2018
Earlier work this paper cites.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman · 2018
Earlier work this paper cites.
Gender bias in coreference resolution: Evaluation and debiasing methods
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang · 2018
Earlier work this paper cites.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
Earlier work this paper cites.
Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le · 2019
Earlier work this paper cites.
Counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology
Ran Zmigrod, Sabrina J. Mielke, Hanna Wallach, and Ryan Cotterell · 2019
Earlier work this paper cites.
Language (technology) is power: A critical survey of “bias” in NLP
Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna Wallach · 2020
Cited alongside, same era.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Cited alongside, same era.
Electra: Pre-training text encoders as discriminators rather than generators
Kevin Clark, Minh-Thang Luong, Quoc V Le, and Christopher D Manning · 2020
Cited alongside, same era.
On measuring and mitigating biased inferences of word embeddings
Sunipa Dev, Tao Li, Jeff M Phillips, and Vivek Srikumar · 2020
Cited alongside, same era.
Araweat: Multidimensional analysis of biases in arabic word embeddings, 2020
Anne Lauscher, Rafik Takieddin, Simone Paolo Ponzetto, and Goran Glavaš · 2020
Cited alongside, same era.
Re-contextualizing fairness in nlp: The case of india
Shaily Bhatt, Sunipa Dev, Partha Talukdar, Shachi Dave, and Vinodkumar Prabhakaran · 2022
Later among the works it cites.
Power to the people? opportunities and challenges for participatory ai
Abeba Birhane, William Isaac, Vinodkumar Prabhakaran, Mark Diaz, Madeleine Clare Elish, Iason Gabriel, and Shakir Mohamed · 2022
Later among the works it cites.
Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al · 2022
Later among the works it cites.
On measures of biases and harms in NLP
Sunipa Dev, Emily Sheng, Jieyu Zhao, Aubrie Amstutz, Jiao Sun, Yu Hou, Mattie Sanseverino, Jiin Kim, Akihiro Nishi, Nanyun Peng, and Kai-Wei Chang · 2022
Later among the works it cites.
Iterative adversarial removal of gender bias in pretrained word embeddings
Yacine Gaci, Boualem Benatallah, Fabio Casati, and Khalid Benabdeslem · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer · 2020
Cited alongside, same era.
UNQOVERing stereotyping biases via underspecified questions
Tao Li, Daniel Khashabi, Tushar Khot, Ashish Sabharwal, and Vivek Srikumar · 2020
Cited alongside, same era.
Crows-pairs: A challenge dataset for measuring social biases in masked language models
Nikita Nangia, Clara Vania, Rasika Bhalerao, and Samuel Bowman · 2020
Cited alongside, same era.
Null it out: Guarding protected attributes by iterative nullspace projection
Shauli Ravfogel, Yanai Elazar, Hila Gonen, Michael Twiton, and Yoav Goldberg · 2020
Cited alongside, same era.
Participation is not a design fix for machine learning, 2020
Mona Sloane, Emanuel Moss, Olaitan Awomolo, and Laura Forlano · 2020
Cited alongside, same era.
On the dangers of stochastic parrots: Can language models be too big?
Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Creative writing with an ai-powered writing assistant: Perspectives from professional writers
Daphne Ippolito, Ann Yuan, Andy Coenen, and Sehmon Burnam · 2022
Later among the works it cites.
Socially aware bias measurements for hindi language representations
Vijit Malik, Sunipa Dev, Akihiro Nishi, Nanyun Peng, and Kai-Wei Chang · 2022
Later among the works it cites.
Cultural incongruencies in artificial intelligence, 2022
Vinodkumar Prabhakaran, Rida Qadri, and Ben Hutchinson · 2022
Later among the works it cites.
Lamda: Language models for dialog applications, 2022
Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, YaGuang Li, Hongrae Lee, Huaixiu Steven Zheng, Amin Ghafouri, Marcelo Menegali, Yanping Huang, Maxim Krikun, Dmitry Lepikhin, James Qin, Dehao Chen, Yuanzhong Xu, Zhifeng Chen, Adam Roberts, Maarten Bosma, Vincent Zhao, Yanqi Zhou, Chung-Ching Chang, Igor Krivokon, Will Rusch, Marc Pickett, Pranesh Srinivasan, Laichee Man, Kathleen Meier-Hellstern, Meredith Ringel Morris, Tulsee Doshi, Renelito Delos Santos, Toju Duke, Johnny Soraker, Ben Zevenbergen, Vinodkumar Prabhakaran, Mark Diaz, Ben Hutchinson, Kristen Olson, Alejandra Molina, Erin Hoffman-John, Josh Lee, Lora Aroyo, Ravi Rajakumar, Alena Butryna, Matthew Lamm, Viktoriya Kuzmina, Joe Fenton, Aaron Cohen, Rachel Bernstein, Ray Kurzweil, Blaise Aguera-Arcas, Claire Cui, Marian Croak, Ed Chi, and Quoc Le · 2022
Later among the works it cites.
Wordcraft: Story writing with large language models
Ann Yuan, Andy Coenen, Emily Reif, and Daphne Ippolito · 2022
Later among the works it cites.
Bias assessment for experts in discrimination, not in computer science
Laura Alonso Alemany, Luciana Benotti, Hernán Maina, Lucía Gonzalez, Lautaro Martínez, Beatriz Busaniche, Alexia Halvorsen, Amanda Rojo, and Mariela Rajngewerc · 2023
Closest in time.
Building stereotype repositories with complementary approaches for scale and depth
Sunipa Dev, Akshita Jha, Jaya Goyal, Dinesh Tewari, Shachi Dave, and Vinodkumar Prabhakaran · 2023
Closest in time.
"i wouldn’t say offensive but…": Disability-centered perspectives on large language models
Vinitha Gadiraju, Shaun Kane, Sunipa Dev, Alex Taylor, Ding Wang, Emily Denton, and Robin Brewer · 2023
Closest in time.
SeeGULL: A stereotype benchmark with broad geo-cultural coverage leveraging generative models
Akshita Jha, Aida Mostafazadeh Davani, Chandan K Reddy, Shachi Dave, Vinodkumar Prabhakaran, and Sunipa Dev · 2023
Closest in time.
Factoring the matrix of domination: A critical review and reimagination of intersectionality in ai fairness, 2023
Anaelia Ovalle, Arjun Subramonian, Vagrant Gautam, Gilbert Gee, and Kai-Wei Chang · 2023
Closest in time.
Ai’s regimes of representation: A community-centered study of text-to-image models in south asia
Rida Qadri, Renee Shelby, Cynthia L. Bennett, and Emily Denton · 2023
Closest in time.