Fetching the paper…
Reading the bibliography…
Despite the impressive performance of current AI models reported across various tasks, performance reports often do not include evaluations of how these models perform on the specific groups that will be impacted by these technologies.
Massively multilingual transfer for ner
Afshin Rahimi, Yuan Li, and Trevor Cohn. 2019 · 1902
Earlier work this paper cites.
The state and fate of linguistic diversity and inclusion in the nlp world
Pratik Joshi, Sebastin Santy, Amar Budhiraja, Kalika Bali, and Monojit Choudhury. 2020 · 2004
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009 · 2009
Earlier work this paper cites.
Indolem and indobert: A benchmark dataset and pre-trained language model for indonesian nlp
Fajri Koto, Afshin Rahimi, Jey Han Lau, and Timothy Baldwin. 2020 · 2011
Earlier work this paper cites.
Decaf: A deep convolutional activation feature for generic visual recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell. 2014 · 2014
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. 2014 · 2014
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 · 2016
Earlier work this paper cites.
Transfer learning from deep features for remote sensing and poverty mapping
Michael Xie, Neal Jean, Marshall Burke, David Lobell, and Stefano Ermon. 2016 · 2016
Earlier work this paper cites.
Neighborhood watch: Using cnns to predict income brackets from google street view images
Ambika Acharya, Helen Fang, and Shubha Raghvendra. 2017 · 2017
Earlier work this paper cites.
Using deep learning and google street view to estimate the demographic makeup of neighborhoods across the united states
Timnit Gebru, Jonathan Krause, Yilun Wang, Duyun Chen, Jia Deng, Erez Lieberman Aiden, and Li Fei-Fei. 2017 · 2017
Earlier work this paper cites.
Openimages: A public dataset for large-scale multi-label and multi-class image classification
Ivan Krasin, Tom Duerig, Neil Alldrin, Vittorio Ferrari, Sami Abu-El-Haija, Alina Kuznetsova, Hassan Rom, Jasper Uijlings, Stefan Popov, Andreas Veit, Serge Belongie, Victor Gomes, Abhinav Gupta, Chen Sun, Gal Chechik, David Cai, Zheyun Feng, Dhyanesh Narayanan, and Kevin Murphy. 2017 · 2017
Earlier work this paper cites.
Making the ai revolution work for everyone
Nicolas Miailhe and Cyrus Hodes. 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.
Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru. 2018 · 2018
Earlier work this paper cites.
Does object recognition work for everyone?
Terrance De Vries, Ishan Misra, Changhan Wang, and Laurens Van der Maaten. 2019 · 2019
Earlier work this paper cites.
Identifying visible actions in lifestyle vlogs
Oana Ignat, Laura Burdick, Jia Deng, and Rada Mihalcea. 2019 · 2019
Earlier work this paper cites.
Digital inequalities in the age of artificial intelligence and big data
Christoph Lutz. 2019 · 2019
Earlier work this paper cites.
Model cards for model reporting
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019 · 2019
Earlier work this paper cites.
Sentence-BERT: Sentence embeddings using Siamese BERT-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
Cited alongside, same era.
Factfulness
Han Rosling, Ola Rosling, and Anna Rosling Rönnlund. 2019 · 2019
Cited alongside, same era.
Transfer learning in natural language processing
Sebastian Ruder, Matthew E. Peters, Swabha Swayamdipta, and Thomas Wolf. 2019 · 2019
Cited alongside, same era.
Ensuring inclusion and diversity in research and research output: A case for a language-sensitive nlp crowdsourcing platform
Dimah Alahmadi, Amal Babour, Kawther Saeedi, and Anna Visvizi. 2020 · 2020
Cited alongside, same era.
Exploring the intersection of the digital divide and artificial intelligence: A hermeneutic literature review
Lemuria Carter, Dapeng Liu, and Caley Cantrell. 2020 · 2020
Cited alongside, same era.
Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare
The effect of gender stereotypes on artificial intelligence recommendations
Jungyong Ahn, Jungwon Kim, and Yongjun Sung. 2022 · 2022
Later among the works it cites.
This is the way: designing and compiling lepiszcze, a comprehensive nlp benchmark for polish
Lukasz Augustyniak, Kamil Tagowski, Albert Sawczyn, Denis Janiak, Roman Bartusiak, Adrian Szymczak, Arkadiusz Janz, Piotr Szymański, Marcin Wątroba, Mikołaj Morzy, et al. 2022 · 2022
Later among the works it cites.
Systematic inequalities in language technology performance across the world’s languages
Damian Blasi, Antonios Anastasopoulos, and Graham Neubig. 2022 · 2022
Later among the works it cites.
On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al. 2022 · 2022
Later among the works it cites.
Artificial intelligence and the digital divide: From an innovation perspective
Irene Kitsara. 2022 · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Davide Cirillo, Silvina Catuara-Solarz, Czuee Morey, Emre Guney, Laia Subirats, Simona Mellino, Annalisa Gigante, Alfonso Valencia, María José Rementeria, Antonella Santuccione Chadha, et al. 2020 · 2020
Cited alongside, same era.
Unsupervised cross-lingual representation learning at scale
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020 · 2020
Cited alongside, same era.
Race and gender
Timnit Gebru. 2020 · 2020
Cited alongside, same era.
Xtreme: A massively multilingual multi-task benchmark for evaluating cross-lingual generalisation
Junjie Hu, Sebastian Ruder, Aditya Siddhant, Graham Neubig, Orhan Firat, and Melvin Johnson. 2020 · 2020
Cited alongside, same era.
Revise: A tool for measuring and mitigating bias in visual datasets
Angelina Wang, Arvind Narayanan, and Olga Russakovsky. 2020 · 2020
Cited alongside, same era.
What vision-language modelssee’when they see scenes
Michele Cafagna, Kees van Deemter, and Albert Gatt. 2021 · 2021
Cited alongside, same era.
The limits of global inclusion in ai development
Alan Chan, Chinasa T Okolo, Zachary Terner, and Angelina Wang. 2021 · 2021
Cited alongside, same era.
Later among the works it cites.
A deep-learning method for the prediction of socio-economic indicators from street-view imagery using a case study from brazil
Jeaneth Machicao, Alison Specht, Danton Vellenich, Leandro Meneguzzi, Romain David, Shelley Stall, Katia Ferraz, Laurence Mabile, Margaret O’brien, and Pedro Corrêa. 2022 · 2022
Later among the works it cites.
Am i a resource-poor language? data sets, embeddings, models and analysis for four different nlp tasks in telugu language
Mounika Marreddy, Subba Reddy Oota, Lakshmi Sireesha Vakada, Venkata Charan Chinni, and Radhika Mamidi. 2022 · 2022
Later among the works it cites.
The dollar street dataset: Images representing the geographic and socioeconomic diversity of the world
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.
When does bias transfer in transfer learning?
Hadi Salman, Saachi Jain, Andrew Ilyas, Logan Engstrom, Eric Wong, and Aleksander Madry. 2022 · 2022
Later among the works it cites.
Exploring gender biases in ml and ai academic research through systematic literature review
Sunny Shrestha and Sanchari Das. 2022 · 2022
Later among the works it cites.
Winoground: Probing vision and language models for visio-linguistic compositionality
Tristan Thrush, Ryan Jiang, Max Bartolo, Amanpreet Singh, Adina Williams, Douwe Kiela, and Candace Ross. 2022 · 2022
Later among the works it cites.
Scalable performance analysis for vision-language models
Santiago Castro, Oana Ignat, and Rada Mihalcea. 2023 · 2023
Closest in time.
Evaluating the diversity, equity, and inclusion of nlp technology: A case study for indian languages
Simran Khanuja, Sebastian Ruder, and Partha Talukdar. 2023 · 2023
Closest in time.
Chatgpt needs spade (sustainability, privacy, digital divide, and ethics) evaluation: A review
Sunder Ali Khowaja, Parus Khuwaja, and Kapal Dev. 2023 · 2023
Closest in time.
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. 2023 · 2023
Closest in time.
Beyond web-scraping: Crowd-sourcing a geographically diverse image dataset
Vikram V Ramaswamy, Sing Yu Lin, Dora Zhao, Aaron B Adcock, Laurens van der Maaten, Deepti Ghadiyaram, and Olga Russakovsky. 2023 · 2023
Closest in time.
Overcoming bias in pretrained models by manipulating the finetuning dataset
Angelina Wang and Olga Russakovsky. 2023 · 2023
Closest in time.