Large image datasets: A pyrrhic win for computer vision?

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works

ImageNet Large Scale Visual Recognition Challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works

Glove: Global Vectors for Word Representation

Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071 · see citing works

Xception: Deep Learning with Depthwise Separable Convolutions

Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380 · see citing works

Distilling the Knowledge in a Neural Network

Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204 · see citing works

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

Deng, J., Guo, J., Xue, N., & Zafeiriou, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00482 · Cited by 7,781 · see citing works

Sorting Things Out: Classification and Its Consequences

Bowker, G. C., & Star, S. L. (1999) · doi:10.7551/mitpress/6352.001.0001 · Cited by 6,188 · see citing works

Places: A 10 Million Image Database for Scene Recognition

Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., & Torralba, A. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2723009 · Cited by 4,042 · see citing works

Large Scale GAN Training for High Fidelity Natural Image Synthesis

Brock, A., Donahue, J., & Simonyan, K. (2018) · arXiv · doi:10.48550/arxiv.1809.11096 · Cited by 3,717 · see citing works

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Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor

Lebovits, H. (2018) · Public Integrity · doi:10.1080/10999922.2018.1511671 · Cited by 3,318 · see citing works

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

Doyle, T. (2017) · The Information Society · doi:10.1080/01972243.2017.1354593 · Cited by 3,110 · see citing works

Algorithms of Oppression: How Search Engines Reinforce Racism

Noble, S. U. (2020) · doi:10.2307/j.ctt1pwt9w5 · Cited by 2,709 · see citing works

Revisiting Unreasonable Effectiveness of Data in Deep Learning Era

Sun, C., Shrivastava, A., Singh, S., & Gupta, A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.97 · Cited by 2,484 · see citing works

Datasheets for Datasets

Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé, H., & Crawford, K. (2018) · arXiv · doi:10.48550/arxiv.1803.09010 · Cited by 2,474

Datasheets for datasets

Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., III, H. D., & Crawford, K. (2021) · Communications of the ACM · doi:10.1145/3458723 · Cited by 2,469 · see citing works

Robust De-anonymization of Large Sparse Datasets

Narayanan, A., & Shmatikov, V. (2008) · 2008 IEEE Symposium on Security and Privacy (sp 2008) · doi:10.1109/sp.2008.33 · Cited by 2,372 · see citing works

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The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scalePreprint

Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.00982 · Cited by 2,194 · see citing works

80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition

Torralba, A., Fergus, R., & Freeman, W. T. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.128 · Cited by 1,921 · see citing works

Data Feminism

D'Ignazio, C., & Klein, L. F. (2020) · doi:10.7551/mitpress/11805.001.0001 · Cited by 1,738 · see citing works

Review of “Race After Technology: Abolitionist Tools for the New Jim Code”

Jipguep-Akhtar, M. C. (2019) · Social Forces · doi:10.1093/sf/soz162 · Cited by 1,482 · see citing works

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Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks

Rothe, R., Timofte, R., & Gool, L. V. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-016-0940-3 · Cited by 904 · see citing works

Estimating the success of re-identifications in incomplete datasets using generative models

Rocher, L., Hendrickx, J. M., & de Montjoye, Y.-A. (2019) · Nature Communications · doi:10.1038/s41467-019-10933-3 · Cited by 816 · see citing works

Do ImageNet Classifiers Generalize to ImageNet?

Recht, B., Roelofs, R., Schmidt, L., & Shankar, V. (2019) · arXiv · doi:10.48550/arxiv.1902.10811 · Cited by 790 · see citing works

PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models

Menon, S., Damian, A., Hu, S., Ravi, N., & Rudin, C. D. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00251 · Cited by 552 · see citing works

Machine Learning Models that Remember Too Much

Song, C., Ristenpart, T., & Shmatikov, V. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3134077 · Cited by 526 · see citing works

Translating Principles into Practices of Digital Ethics: Five Risks of Being Unethical

Floridi, L. (2019) · Philosophy & Technology · doi:10.1007/s13347-019-00354-x · Cited by 524 · cited 1× · see citing works

RetinaFace: Single-stage Dense Face Localisation in the WildPreprint

Deng, J., Guo, J., Zhou, Y., Yu, J., Kotsia, I., & Zafeiriou, S. P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.00641 · Cited by 518 · see citing works

Unequal Representation and Gender Stereotypes in Image Search Results for Occupations

Kay, M., Matuszek, C., & Munson, S. A. (2015) · Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702520 · Cited by 509 · see citing works

The Misgendering Machines: Trans/HCI Implications of Automatic Gender Recognition

Keyes, O. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274357 · Cited by 505 · see citing works

Beyond ‘Revenge Porn’: The Continuum of Image-Based Sexual Abuse

McGlynn, C., Rackley, E., & Houghton, R. (2017) · Feminist Legal Studies · doi:10.1007/s10691-017-9343-2 · Cited by 471 · see citing works

Learning Robust Global Representations by Penalizing Local Predictive Power

Wang, H., Ge, S., Xing, E. P., & Lipton, Z. C. (2019) · arXiv · doi:10.48550/arxiv.1905.13549 · Cited by 337 · see citing works

Consumers and Their Animal Companions

Hirschman, E. C. (1994) · Journal of Consumer Research · doi:10.1086/209374 · Cited by 310 · see citing works

A Systematic Review of the Empirical Literature Evaluating IRBs: What We Know and What We Still Need to Learn

Abbott, L. J., & Grady, C. (2011) · Journal of Empirical Research on Human Research Ethics · doi:10.1525/jer.2011.6.1.3 · Cited by 292 · see citing works

Predictive Inequity in Object DetectionPreprint

Wilson, B., Hoffman, J., & Morgenstern, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.11097 · Cited by 271 · see citing works

Towards fairer datasets: filtering and balancing the distribution of the people subtree in the ImageNet hierarchy

Yang, K., Qinami, K., Fei-Fei, L., Deng, J., & Russakovsky, O. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3375709 · Cited by 254 · see citing works

Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove ThemPreprint

Gonen, H., & Goldberg, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.03862 · Cited by 242 · see citing works

The Origins of Informed Consent: The International Scientific Commission on Medical War Crimes, and the Nuremberg Code

Weindling, P. J. (2001) · Bulletin of the history of medicine · doi:10.1353/bhm.2001.0049 · Cited by 217 · see citing works

VisDA: A Synthetic-to-Real Benchmark for Visual Domain Adaptation

Peng, X., Usman, B., Kaushik, N., Wang, D., Hoffman, J., & Saenko, K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00271 · Cited by 196 · see citing works

Dataset distillation

Wang, T., Zhu, J.-Y., Torralba, A., & Efros, A. A. (2018) · arXiv · doi:10.48550/arxiv.1811.10959 · Cited by 194 · see citing works

Fairness, Equality, and Power in Algorithmic Decision-Making

Kasy, M., & Abebe, R. (2021) · Conference on Fairness, Accountability and Transparency · doi:10.1145/3442188.3445919 · Cited by 193 · see citing works

No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing WorldPreprint

Shankar, S., Halpern, Y., Breck, E. J., Atwood, J. R., Wilson, J., & Sculley, D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.08536 · Cited by 175 · see citing works

The Secretive Company That Might End Privacy as We Know It *

Hill, K. (2022) · Auerbach Publications eBooks · doi:10.1201/9781003278290-26 · Cited by 158 · see citing works

Enacting silence: Residual categories as a challenge for ethics, information systems, and communication

Star, S. L., & Bowker, G. C. (2007) · Ethics and Information Technology · doi:10.1007/s10676-007-9141-7 · Cited by 142 · see citing works

Data Augmentation Using GANsPreprint

Tanaka, F., & Aranha, C. C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.09135 · Cited by 137 · see citing works

Image Pixelization with Differential Privacy

Fan, L. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-95729-6_10 · Cited by 130 · see citing works

Why Do People Volunteer? An Experimental Analysis of Preferences for Time Donations

Brown, A. L., Meer, J., & Williams, J. F. (2018) · Management Science · doi:10.1287/mnsc.2017.2951 · Cited by 120 · see citing works

Tencent ML-Images: A Large-Scale Multi-Label Image Database for Visual Representation Learning

Wu, B., Chen, W., Fan, Y., Zhang, Y., Hou, J., Liu, J., & Zhang, T. (2019) · IEEE Access · doi:10.1109/access.2019.2956775 · Cited by 79 · see citing works

From ImageNet to Image Classification: Contextualizing Progress on BenchmarksPreprint

Tsipras, D., Santurkar, S., Engstrom, L., Ilyas, A., & Mądry, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.11295 · Cited by 76 · see citing works

"My Dog's Just Like Me": Dog Ownership as a Gender Display

Ramirez, M. (2006) · Symbolic Interaction · doi:10.1525/si.2006.29.3.373 · Cited by 71 · see citing works

Algorithmic Injustices: Towards a Relational EthicsPreprint

Birhane, A., & Cummins, F. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.07376 · Cited by 66 · see citing works

Creative Commons Licences, the Copyright Regime and the Online Community: Is there a Fatal Disconnect?

Corbett, S. F. (2011) · Modern Law Review · doi:10.1111/j.1468-2230.2011.00858.x · Cited by 41 · see citing works

Gender and Musical Instrument Stereotypes in Middle School Children

Wrape, E. R., Dittloff, A. L., & Callahan, J. L. (2014) · Update Applications of Research in Music Education · doi:10.1177/8755123314564255 · Cited by 29 · see citing works

Time to discuss consent in digital-data studies

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Investigating the Impact of Inclusion in Face Recognition Training Data on Individual Face Identification

Dulhanty, C., & Wong, A. M. (2020) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3375627.3375875 · Cited by 10 · see citing works

Fonts-2-Handwriting: A Seed-Augment-Train framework for universal digit classificationPreprint

Prabhu, V. U., Han, S., Yap, D. A., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.08633 · Cited by 5 · see citing works

Informed Consent the Global Picture

Blain, C., Mackay, M., & Tanner, J. (2002) · Journal of Perioperative Practice · doi:10.1177/175045890201201101 · Cited by 4 · see citing works

Informed consent for photography in dental practice : communication

Naidoo, S. (2009) · Cited by 2 · see citing works

Creative Commons Licences: A Symptom or a Cause?Preprint

Corbett, S. F. (2009) · SSRN Electronic Journal · doi:10.2139/ssrn.2028726 · Cited by 2 · see citing works

Where Have We Been? Where Are We Going?

Tedrow, M. K. (2017) · Routledge eBooks · doi:10.4324/9781315167572-4 · Cited by 2 · see citing works

The School Girl, the Billboard, and Virgin: The Virgin Mobile Case and the Use of Creative Commons Licensed Photographs by Commercial Entities

Carroll, E., & Coates, J. (2011) · QUT ePrints (Queensland University of Technology) · Cited by 1 · see citing works

On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988 · see citing works

On the genealogy of machine learning datasets: A critical history of ImageNet

Denton, E., Hanna, A., Amironesei, R., Smart, A., & Nicole, H. (2021) · Big Data & Society · doi:10.1177/20539517211035955 · Cited by 221 · see citing works

The algorithm audit: Scoring the algorithms that score us

Brown, S., Davidovic, J., & Hasan, A. (2021) · Big Data & Society · doi:10.1177/2053951720983865 · Cited by 204 · see citing works

Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models

Schramowski, P., Brack, M., Deiseroth, B., & Kersting, K. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.02157 · Cited by 219 · see citing works

Multimodal datasets: misogyny, pornography, and malignant stereotypes

Birhane, A., Prabhu, V. U., & Kahembwe, E. (2021) · arXiv · doi:10.48550/arxiv.2110.01963 · Cited by 160 · see citing works

Muse: Text-To-Image Generation via Masked Generative Transformers

Chang, H., Zhang, H., Barber, J., Maschinot, A., Lezama, J., Jiang, L., Yang, M.-H., Murphy, K., Freeman, W. T., Rubinstein, M., et al. (2023) · arXiv · doi:10.48550/arxiv.2301.00704 · Cited by 157 · see citing works

Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generators

Liesenfeld, A., Lopez, A., & Dingemanse, M. (2023) · Proceedings of the 5th International Conference on Conversational User Interfaces · doi:10.1145/3571884.3604316 · Cited by 111 · see citing works

Rethinking open source generative AI: open washing and the EU AI Act

Liesenfeld, A., & Dingemanse, M. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3659005 · Cited by 67 · see citing works

Data and its (dis)contents: A survey of dataset development and use in machine learning research

Paullada, A., Raji, I. D., Bender, E. M., Denton, E., & Hanna, A. (2021) · Patterns · doi:10.1016/j.patter.2021.100336 · Cited by 548 · see citing works

Data Governance in the Age of Large-Scale Data-Driven Language Technology

Jernite, Y., Nguyen, H., Biderman, S., Rogers, A., Masoud, M., Danchev, V., Tan, S., Luccioni, A. S., Subramani, N., Johnson, I., et al. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3534637 · Cited by 45 · see citing works

Taxonomizing and Measuring Representational Harms: A Look at Image Tagging

Katzman, J., Wang, A., Scheuerman, M., Blodgett, S. L., Laird, K., Wallach, H., & Barocas, S. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i12.26670 · Cited by 32 · see citing works

The Dark Side of Dataset Scaling: Evaluating Racial Classification in Multimodal Models

Birhane, A., Dehdashtian, S., Prabhu, V., & Boddeti, V. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3658968 · Cited by 28 · see citing works

‘We can see a savage’: a case study of the colonial gaze in generative AI algorithms

Alenichev, A., Shaffer, J. D., Kingori, P., Grietens, K. P., Muldoon, J., & Rocher, L. (2026) · AI & SOCIETY · doi:10.1007/s00146-025-02685-0 · Cited by 3 · see citing works

Engaging Communities Meaningfully in Defining Disability Representation for AI Image Generation

Thieme, A., Faia Marques, R., Grayson, M., Balachandar, S., Tyler Cassidy, C., Choksi, M. Z., Longden, C., Huda, R. S., Ileve Kalovwe, N., Mallon, C., et al. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790768 · Cited by 1 · see citing works

The U.S. decennial census

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Corrective Method for Status-Quo Bias by User’s Inputting Reason for Selection and Presenting Criticism Information

Shimizu, T., Futami, K., Terada, T., & Tsukamoto, M. (2023) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-40978-3_28

The Census of Ireland, 1821-1911: General Reports and Extracts

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Innovative Approaches to Census-Taking: Overview of the 2011 Census Round in Europe

Valente, P. (2014) · Studies in theoretical and applied statistics. Selected papers of the statistical societies · doi:10.1007/978-3-319-05552-7_17 · Cited by 9 · see citing works

Editor's Corner

Sare, L. (2020) · DttP Documents to the People · doi:10.5860/dttp.v48i1.7330