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Negin Ghavami and Letitia Anne Peplau. 2013. An Intersectional Analysis of Gender and Ethnic Stereotypes: Testing
Three Hypotheses. Psychology of Women Quarterly 37, 1 (2013), 113–127. https://doi.org/10.1177/0361684312464203
arXiv:https://doi.org/10.1177/0361684312464203
Amir Gholami, Sehoon Kim, Zhen Dong, Zhewei Yao, Michael W Mahoney, and Kurt Keutzer. 2021. A Survey of Quantization
Methods for Efficient Neural Network Inference. arXiv preprint arXiv:2103.13630 (2021).
Amirata Ghorbani and James Zou. 2019. Data shapley: Equitable valuation of data for machine learning. In International
Conference on Machine Learning. PMLR, 2242–2251.
James J Gibson. 1979. The ecological approach to visual perception. Psychology Press.
Talia B Gillis and Jann L Spiess. 2019. Big data and discrimination. The University of Chicago Law Review 86, 2 (2019),
459–488.
Antonio Ginart, Melody Y. Guan, Gregory Valiant, and James Zou. 2019. Making AI Forget You: Data Deletion in Machine
Learning. arXiv:1907.05012 [cs.LG]
Kathryn T. Gines. 2011. Black Feminism and Intersectional Analyses. Philosophy Today 55, 9999 (2011), 275–284. https:
//doi.org/10.5840/philtoday201155supplement68
Jane C Ginsburg and Luke Ali Budiardjo. 2019. Authors and machines. Berkeley Tech. LJ 34 (2019), 343.
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. 2014. Rich feature hierarchies for accurate object detection
and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition. 580–587.
Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In
International Conference on Artificial Intelligence and Statistics.
Abhinav Goel, Caleb Tung, Yung-Hsiang Lu, and George K Thiruvathukal. 2020b. A survey of methods for low-power deep
learning and computer vision. In 2020 IEEE 6th World Forum on Internet of Things (WF-IoT). IEEE, 1–6.
Karan Goel, Albert Gu, Yixuan Li, and Christopher Ré. 2020a. Model Patching: Closing the Subgroup Performance Gap with
Data Augmentation. arXiv preprint arXiv:2008.06775 (2020).
Karan Goel, Nazneen Rajani, Jesse Vig, Samson Tan, Jason Wu, Stephan Zheng, Caiming Xiong, Mohit Bansal, and Christopher
Ré. 2021. Robustness Gym: Unifying the NLP Evaluation Landscape. arXiv preprint arXiv:2101.04840 (2021).
Gabriel Goh, Nick Cammarata, Chelsea Voss, Shan Carter, Michael Petrov, Ludwig Schubert, Alec Radford, and Chris Olah.
2021. Multimodal neurons in artificial neural networks. Distill 6, 3 (2021), e30.
Seraphina Goldfarb-Tarrant, Rebecca Marchant, Ricardo Muñoz Sánchez, Mugdha Pandya, and Adam Lopez. 2021. Intrinsic
Bias Metrics Do Not Correlate with Application Bias. In Proceedings of the 59th Annual Meeting of the Association for
Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long
Papers). Association for Computational Linguistics, Online, 1926–1940. https://doi.org/10.18653/v1/2021.acl-long.150
Hila Gonen and Yoav Goldberg. 2019. Lipstick on a pig: Debiasing methods cover up systematic gender biases in word
embeddings but do not remove them. In Proceedings of NAACL 2019.
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep learning. MIT press.
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and
Yoshua Bengio. 2014. Generative Adversarial Nets. In Advances in