[189] Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, and Ed Chi. Fairness without demographics through adversarially reweighted learning. Advances in Neural Information Processing Systems (NeurIPS), 33:728-740, 2020.
[190] Anjana Lakshmi, Bernd Wittenbrink, Joshua Correll, and Debbie S Ma. The india face set: International and cultural boundaries impact face impressions and perceptions of category membership. Frontiers in psychology, 12:161, 2021.
[191] Min Kyung Lee and Katherine Rich. Who is included in human perceptions of ai?: Trust and perceived fairness around healthcare ai and cultural mistrust. In Conference on Human Factors in Computing Systems (CHI), pages 1-14, 2021.
[192] John Leuner. A replication study: Machine learning models are capable of predicting sexual orientation from facial images. arXiv preprint arXiv:1902.10739, 2019.
[193] Gil Levi and Tal Hassner. Age and gender classification using convolutional neural networks. In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 34-42, 2015.
[194] Jizhizi Li, Sihan Ma, Jing Zhang, and Dacheng Tao. Privacy-preserving portrait matting. In ACM International Conference on Multimedia, pages 3501-3509, 2021.
[195] Tao Li and Lei Lin. Anonymousnet: Natural face de-identification with measurable privacy. In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019.
[196] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European Conference on Computer Vision (ECCV), pages 740-755. Springer, 2014.
[197] Zhenyi Liu, Trisha Lian, Joyce Farrell, and Brian A. Wandell. Neural network generalization: The impact of camera parameters. IEEE Access, 8:10443-10454, 2020.
[198] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaou Tang. Deep learning face attributes in the wild. In IEEE International Conference on Computer Vision (ICCV), pages 3730-3738, 2015.
[199] Duri Long and Brian Magerko. What is ai literacy? competencies and design considerations. In Proceedings of the 2020 CHI conference on human factors in computing systems, pages 1-16, 2020.
[200] Zhongyu Lou, Fares Alnajjar, Jose M Alvarez, Ninghang Hu, and Theo Gevers. Expression-invariant age estimation using structured learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(2):365-375, 2017.
[201] Mike Loukides, Hilary Mason, and D Patil. Of oaths and checklists. https:// www.oreilly.com/radar/of-oaths-and-checklists/, 2018. [Accessed August 7, 2023].
[202] Alexandra Sasha Luccioni, Frances Corry, Hamsini Sridharan, Mike Ananny, Jason Schultz, and Kate Crawford. A framework for deprecating datasets: Standardizing documentation, identification, and communication. In ACM Conference on Fairness, Accountability, and Transparency (FAccT), pages 199-212, 2022.
[203] Debbie S Ma, Joshua Correll, and Bernd Wittenbrink. The chicago face database: A free stimulus set of faces and norming data. Behavior research methods, 47(4):1122-1135, 2015.
[204] Debbie S Ma, Justin Kantner, and Bernd Wittenbrink. Chicago face database: Multiracial expansion. Behavior Research Methods, 53(3):1289-1300, 2021.
[205] Michael A Madaio, Luke Stark, Jennifer Wortman Vaughan, and Hanna Wallach. Co-designing checklists to understand organizational challenges and opportunities around fairness in ai. In Proceedings of the 2020 CHI conference on human factors in computing systems, pages 1-14, 2020.