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Models that are learned from real-world data are often biased because the data used to train them is biased.
The validity and practicality of sun-reactive skin types i through vi
Fitzpatrick, Thomas B · 1988
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Performance characterization in computer vision
Haralick, Robert M · 1992
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Gaussian processes in machine learning
Rasmussen, Carl Edward · 2004
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Brochu, Eric, Cora, Vlad M, and De Freitas, Nando · 2010
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Unbiased look at dataset bias
Torralba, Antonio and Efros, Alexei A · 2011
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Fairness through awareness
Dwork, Cynthia, Hardt, Moritz, Pitassi, Toniann, Reingold, Omer, and Zemel, Richard · 2012
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Face recognition performance: Role of demographic information
Klare, Brendan F, Burge, Mark J, Klontz, Joshua C, Bruegge, Richard W Vorder, and Jain, Anil K · 2012
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Practical bayesian optimization of machine learning algorithms
Snoek, Jasper, Larochelle, Hugo, and Adams, Ryan P · 2012
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Conditional generative adversarial nets
Mirza, Mehdi and Osindero, Simon · 2014
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Virtual and real world adaptation for pedestrian detection
Vazquez, David, Lopez, Antonio M, Marin, Javier, Ponsa, Daniel, and Geronimo, David · 2014
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Pushing the frontiers of unconstrained face detection and recognition: Iarpa janus benchmark a
Klare, Brendan F, Klein, Ben, Taborsky, Emma, Blanton, Austin, Cheney, Jordan, Allen, Kristen, Grother, Patrick, Mah, Alan, and Jain, Anil K · 2015
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Dex: Deep expectation of apparent age from a single image
Rothe, Rasmus, Timofte, Radu, and Van Gool, Luc · 2015
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Data and algorithmic bias in the web
Baeza-Yates, Ricardo · 2016
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The perpetual line-up: Unregulated police face recognition in america
Garvie, Clare · 2016
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Ms-celeb-1m: A dataset and benchmark for large-scale face recognition
Guo, Yandong, Zhang, Lei, Hu, Yuxiao, He, Xiaodong, and Gao, Jianfeng · 2016
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Algorithmic bias: From discrimination discovery to fairness-aware data mining
Hajian, Sara, Bonchi, Francesco, and Castillo, Carlos · 2016
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Discovering blind spots of predictive models: Representations and policies for guided exploration
Lakkaraju, Himabindu, Kamar, Ece, Caruana, Rich, and Horvitz, Eric · 2016
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Model-driven simulations for deep convolutional neural networks
Veeravasarapu, VSR, Rothkopf, Constantin, and Ramesh, Visvanathan · 2016
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The space of transferable adversarial examples
Tramèr, Florian, Papernot, Nicolas, Goodfellow, Ian, Boneh, Dan, and McDaniel, Patrick · 2017
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Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks
Zhang, Han, Xu, Tao, Li, Hongsheng, Zhang, Shaoting, Wang, Xiaogang, Huang, Xiaolei, and Metaxas, Dimitris N · 2017
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A coverage-based utility model for identifying unknown unknowns
Bansal, Gagan and Weld, Daniel S · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Buolamwini, Joy and Gebru, Timnit · 2018
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Stargan: Unified generative adversarial networks for multi-domain image-to-image translation
Choi, Yunjey, Choi, Minje, Kim, Munyoung, Ha, Jung-Woo, Kim, Sunghun, and Choo, Jaegul · 2018
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Attribute2image: Conditional image generation from visual attributes
Yan, Xinchen, Yang, Jimei, Sohn, Kihyuk, and Lee, Honglak · 2016
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Synthesizing robust adversarial examples
Athalye, Anish, Engstrom, Logan, Ilyas, Andrew, and Kwok, Kevin · 2017
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Gender shades: intersectional phenotypic and demographic evaluation of face datasets and gender classifiers
Buolamwini, Joy Adowaa · 2017
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Semantics derived automatically from language corpora contain human-like biases
Caliskan, Aylin, Bryson, Joanna J, and Narayanan, Arvind · 2017
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Unsupervised image-to-image translation with generative adversarial networks
Dong, Hao, Neekhara, Paarth, Wu, Chao, and Guo, Yike · 2017
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Identifying unknown unknowns in the open world: Representations and policies for guided exploration
Lakkaraju, Himabindu, Kamar, Ece, Caruana, Rich, and Horvitz, Eric · 2017
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Improving smiling detection with race and gender diversity
Ryu, Hee Jung, Mitchell, Margaret, and Adam, Hartwig · 2017
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Progressive growing of GANs for improved quality, stability, and variation
Karras, Tero, Aila, Timo, Laine, Samuli, and Lehtinen, Jaakko · 2018
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Fair, transparent, and accountable algorithmic decision-making processes
Lepri, Bruno, Oliver, Nuria, Letouzé, Emmanuel, Pentland, Alex, and Vinck, Patrick · 2018
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Identifying bias in ai using simulation
McDuff, Daniel, Cheng, Roger, and Kapoor, Ashish · 2018
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Robustness of conditional gans to noisy labels
Thekumparampil, Kiran K, Khetan, Ashish, Lin, Zinan, and Oh, Sewoong · 2018
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Attngan: Fine-grained text to image generation with attentional generative adversarial networks
Xu, Tao, Zhang, Pengchuan, Huang, Qiuyuan, Zhang, Han, Gan, Zhe, Huang, Xiaolei, and He, Xiaodong · 2018
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Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial ai products
Raji, Inioluwa Deborah and Buolamwini, Joy · 2019
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Predictive Inequity in Object Detection
Wilson, Benjamin, Hoffman, Judy, and Morgenstern, Jamie · 2019
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