[172] Matthew Kay, Cynthia Matuszek, and Sean A Munson. Unequal representation and gender stereotypes in image search results for occupations. In *Conference on Human Factors in Computing Systems (CHI)*, pages 3819-3828, 2015.
[173] Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, et al. The kinetics human action video dataset. arXiv preprint arXiv:1705.06950, 2017.
[174] Jane Kaye, Edgar A Whitley, David Lund, Michael Morrison, Harriet Teare, and Karen Melham. Dynamic consent: a patient interface for twenty-first century research networks. European journal of human genetics, 23(2):141-146, 2015.
[175] Ira Kemelmacher-Shlizerman, Steven M Seitz, Daniel Miller, and Evan Brossard. The megaface benchmark: 1 million faces for recognition at scale. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 4873-4882, 2016.
[176] Norbert L Kerr. Harking: Hypothesizing after the results are known. *Personality and social psychology review*, 2(3):196-217, 1998.
[177] Suzanne J Kessler and Wendy McKenna. Gender: An ethnomethodological approach. University of Chicago Press, 1985.
[178] Florian Keusch. The influence of answer box format on response behavior on list-style open-ended questions. Journal of Survey Statistics and Methodology, 2(3):305-322, 2014.
[179] Os Keyes. The misgendering machines: Trans/hci implications of automatic gender recognition. Proceedings of the ACM on human-computer interaction, 2(CSCW):1-22, 2018.
[180] Zaid Khan and Yun Fu. One label, one billion faces: Usage and consistency of racial categories in computer vision. In ACM Conference on Fairness, Accountability, and Transparency (FAccT), pages 587-597, 2021.
[181] Won Kim, Byoung-Ju Choi, Eui-Kyeong Hong, Soo-Kyung Kim, and Doheon Lee. A taxonomy of dirty data. Data mining and knowledge discovery, 7:81-99, 2003.
[182] Jennifer Klima, Sara M Fitzgerald-Butt, Kelly J Kelleher, Deena J Chisolm, R Dawn Comstock, Amy K Ferketich, and Kim L McBride. Understanding of informed consent by parents of children enrolled in a genetic biobank. Genetics in Medicine, 16(2):141-148, 2014.
[183] Bernard Koch, Emily Denton, Alex Hanna, and Jacob Gates Foster. Reduced, reused and recycled: The life of a dataset in machine learning research. In Advances in Neural Information Processing Systems Datasets and Benchmarks Track (NeurIPS D&B), 2021.
[184] Adam Kortylewski, Bernhard Egger, Andreas Schneider, Thomas Gerg, Andreas Morel-Forster, and Thomas Vetter. Empirically analyzing the effect of dataset biases on deep face recognition systems. In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 2093–2102, 2018.
[185] Adam Kortylewski, Bernhard Egger, Andreas Schneider, Thomas Gerig, Andreas Morel-Forster, and Thomas Vetter. Analyzing and reducing the damage of dataset bias to face recognition with synthetic data. In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019.
[186] Matthew B Kugler. From identification to identity theft: Public perceptions of biometric privacy harms. *UC Irvine L. Rev.*, 10:107, 2019.
[187] Neeraj Kumar, Alexander C Berg, Peter N Belhumeur, and Shree K Nayar. Attribute and simile classifiers for face verification. In IEEE/CVF International Conference on Computer Vision (ICCV), pages 365-372, 2009.
[188] Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Alexander Kolesnikov, et al. The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale. International Journal of Computer Vision (IJCV), 128(7):1956-1981, 2020.