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Machine learning is traditionally studied at the model level: researchers measure and improve the accuracy, robustness, bias, efficiency, and other dimensions of specific models.
Manipulation-proof machine learning, 2020
Daniel Björkegren, Joshua E. Blumenstock, and Samsun Knight · 2004
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts · 2011
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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, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al · 2012
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Explaining and harnessing adversarial examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Racial bias in health care and health
David R. Williams and Ronald Wyatt · 2015
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Big data’s disparate impact
Solon Barocas and Andrew D. Selbst · 2016
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Racial and ethnic disparities in the quality of health care
Kevin Fiscella and Mechelle R. Sanders · 2016
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Training deep networks for facial expression recognition with crowd-sourced label distribution
Emad Barsoum, Cha Zhang, Cristian Canton-Ferrer, and Zhengyou Zhang · 2016
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From facial expression recognition to interpersonal relation prediction
Zhanpeng Zhang, Ping Luo, Chen Change Loy, and Xiaoou Tang · 2016
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Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva, Brett Kuprel, Roberto Novoa, Justin Ko, Susan Swetter, Helen Blau, and Sebastian Thrun · 2017
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The eu general data protection regulation (gdpr)
Paul Voigt and Axel von dem Bussche · 2017
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Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild
Shan Li, Weihong Deng, and Junping Du · 2017
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Affectnet: A database for facial expression, valence, and arousal computing in the wild
Ali Mollahosseini, Behzad Hassani, and Mohammad H. Mahoor · 2017
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
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The ham10000 dataset: A large collection of multi-source dermatoscopic images of common pigmented skin lesions
Philipp Tschandl, Cliff Rosendahl, and Harald Kittler · 2018
Cited alongside, same era.
Jakobovski/free-spoken-digit-dataset: v1.0.8, August 2018
Zohar Jackson, César Souza, Jason Flaks, Yuxin Pan, Hereman Nicolas, and Adhish Thite · 2018
Cited alongside, same era.
Interpreting and explaining deep neural networks for classification of audio signals
Sören Becker, Marcel Ackermann, Sebastian Lapuschkin, Klaus-Robert Müller, and Wojciech Samek · 2018
Cited alongside, same era.
The paradox of automation as anti-bias intervention
Ifeoma Ajunwa · 2019
Cited alongside, same era.
Emotional expressions reconsidered: Challenges to inferring emotion from human facial movements
Lisa Feldman Barrett, Ralph Adolphs, Stacy Marsella, Aleix M. Martinez, and Seth D. Pollak · 2019
Cited alongside, same era.
Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations
Laleh Seyyed-Kalantari, Haoran Zhang, Matthew BA McDermott, Irene Y Chen, and Marzyeh Ghassemi · 2021
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Distribution of race and fitzpatrick skin types in data sets for deep learning in dermatology: A systematic review
Yong-hun Kim, Ajdin Kobic, and Nahid Y. Vidal · 2021
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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, Erik Brynjolfsson, S. Buch, D. Card, Rodrigo Castellon, Niladri S. Chatterji, Annie Chen, Kathleen Creel, Jared Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren E. Gillespie, Karan Goel, Noah D. Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas F. Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, O. Khattab, Pang Wei Koh, Mark S. Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir P. Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, J. F. Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, Aditi Raghunathan, Robert Reich, Hongyu Ren, Frieda Rong, Yusuf H. Roohani, Camilo Ruiz, Jackson K. Ryan, Christopher R’e, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishna Parasuram Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei A. Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, and Percy Liang · 2021
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Dissecting racial bias in an algorithm used to manage the health of populations
Ziad Obermeyer, Brian Powers, Christine Vogeli, and Sendhil Mullainathan · 2019
Cited alongside, same era.
Speech model pre-training for end-to-end spoken language understanding
Loren Lugosch, Mirco Ravanelli, Patrick Ignoto, Vikrant Singh Tomar, and Yoshua Bengio · 2019
Cited alongside, same era.
Racial disparities in automated speech recognition
Allison Koenecke, Andrew Joo Hun Nam, Emily Lake, Joe Nudell, Minnie Quartey, Zion Mengesha, Connor Toups, John R. Rickford, Dan Jurafsky, and Sharad Goel · 2020
Cited alongside, same era.
Augment intelligence dermatology : Deep neural networks empower medical professionals in diagnosing skin cancer and predicting treatment options for 134 skin disorders
Seung Han, Ilwoo Park, Sung Chang, Woohyung Lim, Myoung Kim, Gyeong Park, Jebyeong Chae, Chang-Hun Huh, and Jung-Im Na · 2020
Cited alongside, same era.
Hidden in plain sight — reconsidering the use of race correction in clinical algorithms
Darshali A. Vyas, Leo G. Eisenstein, and David S. Jones · 2020
Cited alongside, same era.
An investigation of why overparameterization exacerbates spurious correlations
Shiori Sagawa, Aditi Raghunathan, Pang Wei Koh, and Percy Liang · 2020
Cited alongside, same era.
Enrollment algorithms are contributing to the crises of higher education
Alex Engler · 2021
Cited alongside, same era.
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Picking on the same person: Does algorithmic monoculture lead to outcome homogenization?
Rishi Bommasani, Kathleen Creel, Ananya Kumar, Dan Jurafsky, and Percy Liang · 2022
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HAPI: A Large-scale Longitudinal Dataset of Commercial ML API Predictions
Lingjiao Chen, Zhihua Jin, Evan Sabri Eyuboglu, Christopher Ré, Matei Zaharia, and James Y Zou · 2022
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Disparities in dermatology ai performance on a diverse, curated clinical image set
Roxana Daneshjou, Kailas Vodrahalli, Roberto A. Novoa, Melissa Jenkins, Weixin Liang, Veronica Rotemberg, Justin Ko, Susan M. Swetter, Elizabeth E. Bailey, Olivier Gevaert, Pritam Mukherjee, Michelle Phung, Kiana Yekrang, Bradley Fong, Rachna Sahasrabudhe, Johan A. C. Allerup, Utako Okata-Karigane, James Zou, and Albert S. Chiou · 2022
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The algorithmic leviathan: Arbitrariness, fairness, and opportunity in algorithmic decision-making systems
Kathleen Creel and Deborah Hellman · 2022
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Should attention be all we need? the epistemic and ethical implications of unification in machine learning
Nic Fishman and Leif Hancox-Li · 2022
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Patient race or ethnicity and the use of diagnostic imaging: A systematic review
Rebecca L. Colwell, Anand K. Narayan, and Andrew B. Ross · 2022
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Overcoming bias in pretrained models by manipulating the finetuning dataset
Angelina Wang and Olga Russakovsky · 2023
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Algorithmic pluralism: A structural approach towards equal opportunity
Shomik Jain, Vinith M. Suriyakumar, Kathleen Creel, and Ashia C. Wilson · 2023
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Ecosystem graphs: The social footprint of foundation models
Rishi Bommasani, Dilara Soylu, Thomas I Liao, Kathleen A Creel, and Percy Liang · 2023
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Advances in ai: Are we ready for a tech revolution?
Aleksander Mądry · 2023
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The right to be an exception to a data-driven rule
Sarah H Cen and Manish Raghavan · 2023
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