Towards fairer datasets: filtering and balancing the distribution of the people subtree in the ImageNet hierarchy
Open accessAbstract
Computer vision technology is being used by many but remains representative of only a few. People have reported misbehavior of computer vision models, including offensive prediction results and lower performance for underrepresented groups. Current computer vision models are typically developed using datasets consisting of manually annotated images or videos; the data and label distributions in these datasets are critical to the models' behavior. In this paper, we examine ImageNet, a large-scale ontology of images that has spurred the development of many modern computer vision methods. We consider three key factors within the person subtree of ImageNet that may lead to problematic behavior in downstream computer vision technology: (1) the stagnant concept vocabulary of WordNet, (2) the attempt at exhaustive illustration of all categories with images, and (3) the inequality of representation in the images within concepts. We seek to illuminate the root causes of these concerns and take the first steps to mitigate them constructively.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The ImageNet data is mostly Western, and encodes several gender/ethnicity stereotypes with under-representation of certain groups.
“The data is mostly Western, and encodes several gender/ethnicity stereotypes with under-representation of certain groups [47].”
From Intriguing Properties of Vision Transformers (2021) · cites this paper as [47]
The available copy’s appendix has a section headed “Problem 3: Lack of Image Diversity,” but its passage only describes an annotation interface. The section on sensitive synsets discusses gender and ethnicity labels, not stereotypes in the images. Searches for Western predominance, stereotypes, and under-representation found no passage establishing those claims; the main-paper findings may be missing from the available text. (LLM, conf=0.72)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · 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
Self-citationYou Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · see citing works
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460 · 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
Self-citationGirshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105 · see citing works
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599 · see citing works
Deep Learning Face Attributes in the Wild
Liu, Z., Luo, P., Wang, X., & Tang, X. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.425 · Cited by 7,747 · see citing works
Show and tell: A neural image caption generator
Vinyals, O., Toshev, A., Bengio, S., & Erhan, D. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298935 · Cited by 6,318 · see citing works
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., Shamma, D. A., et al. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-016-0981-7 · Cited by 5,107 · see citing works
Self-citationThe validity and practicality of sun-reactive skin types I through VI
Fitzpatrick, T. B. (1988) · Archives of Dermatology · doi:10.1001/archderm.1988.01670060015008 · Cited by 4,339 · 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
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012) · Proceedings of the 3rd Innovations in Theoretical Computer Science Conference · doi:10.1145/2090236.2090255 · Cited by 3,501 · see citing works
Barocas, S., & Selbst, A. D. (2014) · doi:10.2139/ssrn.2477899 · Cited by 3,048 · see citing works
Concreteness, imagery, and meaningfulness values for 925 nouns.
Paivio, A., Yuille, J. C., & Madigan, S. A. (1968) · Journal of Experimental Psychology · doi:10.1037/h0025327 · Cited by 2,855 · see citing works
Torralba, A., & Efros, A. A. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995347 · Cited by 2,440 · 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
Certifying and Removing Disparate Impact
Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., & Venkatasubramanian, S. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2783311 · Cited by 1,827 · see citing works
Mitigating Unwanted Biases with Adversarial Learning
Zhang, B. H., Lemoine, B., & Mitchell, M. (2018) · Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3278721.3278779 · Cited by 1,387 · see citing works
Large-Scale Long-Tailed Recognition in an Open World
Liu, Z., Miao, Z., Zhan, X., Wang, J., Gong, B., & Yu, S. X. (2019) · 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/cvpr.2019.00264 · Cited by 1,169 · see citing works
Deep convolutional neural fields for depth estimation from a single image
Liu, F., Shen, C., & Lin, G. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299152 · Cited by 892 · see citing works
Age-of-acquisition, imagery, concreteness, familiarity, and ambiguity measures for 1,944 words
Gilhooly, K. J., & Logie, R. H. (1980) · Behavior Research Methods · doi:10.3758/bf03201693 · Cited by 877 · see citing works
Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K.-W. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1323 · Cited by 829 · see citing works
Age and Gender Estimation of Unfiltered Faces
Eidinger, E., Enbar, R., & Hassner, T. (2014) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2014.2359646 · Cited by 814 · see citing works
Wang, Y., & Kosinski, M. (2018) · Journal of Personality and Social Psychology · doi:10.1037/pspa0000098 · Cited by 766 · cited 1× · see citing works
“Examples range from quantifying beauty [47] to predicting sexual orientation [76] to (arguably) annotating gender [25, 42].”
BackgroundThree naive Bayes approaches for discrimination-free classification.
Calders, T., & Verwer, S. (2010) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-010-0190-x · Cited by 764 · see citing works
Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A
Klare, B. F., Klein, B., Taborsky, E., Blanton, A., Cheney, J., Allen, K., Grother, P. J., Mah, A., Burge, M. J., & Jain, A. K. (2015) · 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/cvpr.2015.7298803 · Cited by 706 · see citing works
Discrimination-aware data mining
Pedreshi, D., Ruggieri, S., & Turini, F. (2008) · Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1401890.1401959 · Cited by 676 · see citing works
Demographics and Dynamics of Mechanical Turk Workers
Difallah, D. E., Filatova, E., & Ipeirotis, P. G. (2018) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3159652.3159661 · 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
Fairness-aware Learning through Regularization Approach
Kamishima, T., Akaho, S., & Sakuma, J. (2011) · 2011 IEEE 11th International Conference on Data Mining Workshops · doi:10.1109/icdmw.2011.83 · Cited by 475 · see citing works
Building Classifiers with Independency Constraints
Calders, T., Kamiran, F., & Pechenizkiy, M. (2009) · 2009 IEEE International Conference on Data Mining Workshops · doi:10.1109/icdmw.2009.83 · Cited by 468 · see citing works
Classifying without discriminating
Kamiran, F., & Calders, T. (2009) · 2009 2nd International Conference on Computer, Control and Communication · doi:10.1109/ic4.2009.4909197 · Cited by 409 · see citing works
Women Also Snowboard: Overcoming Bias in Captioning Models
Hendricks, L. A., Burns, K., Saenko, K., Darrell, T., & Rohrbach, A. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01219-9_47 · Cited by 404 · see citing works
Bird, H., Franklin, S., & Howard, D. (2001) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03195349 · Cited by 388 · see citing works
Imageability ratings for 3,000 monosyllabic words
Cortese, M. J., & Fugett, A. D. (2004) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03195585 · Cited by 335 · see citing works
Altarriba, J., Bauer, L., & Benvenuto, C. (1999) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03200738 · Cited by 330 · see citing works
Abstractness, imagery, and meaningfulness in paired-associate learning
Paivio, A. (1965) · Journal of Verbal Learning and Verbal Behavior · doi:10.1016/s0022-5371(65)80064-0 · Cited by 319 · see citing works
50 Years of Test (Un)fairness: Lessons for Machine Learning
Hutchinson, B., & Mitchell, M. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287600 · Cited by 297 · see citing works
Racial Influence on Automated Perceptions of EmotionsPreprint
Rhue, L. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3281765 · Cited by 190 · see citing works
ConvNets and ImageNet Beyond Accuracy: Understanding Mistakes and Uncovering Biases
Stock, P., & Cissé, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01231-1_31 · Cited by 166 · see citing works
Measurement of skin color: practical application and theoretical considerations.
Takiwaki, H. (1998) · PubMed · Cited by 132 · see citing works
Dewaele, J. (2016) · Journal of Pragmatics · doi:10.1016/j.pragma.2016.01.009 · Cited by 111 · see citing works
Inside the image factory: stock photography and cultural production
Frosh, P. (2001) · Media Culture & Society · doi:10.1177/016344301023005005 · Cited by 67 · see citing works
Yee, L. T. S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0174569 · Cited by 52 · see citing works
Rating Offensive Words in Three Television Program Contexts
Sapolsky, B. S., Shafer, D. M., & Kaye, B. K. (2010) · Mass Communication & Society · doi:10.1080/15205430903359693 · Cited by 41 · see citing works
When corporations come to define the visual politics of gender
Aiello, G., & Woodhouse, A. (2016) · Journal of Language and Politics · doi:10.1075/jlp.15.3.08aie · Cited by 32 · see citing works
Frosh, P. (2002) · Journal of Consumer Culture · doi:10.1177/146954050200200202 · Cited by 16 · see citing works
108th Congress 1st Session H. R. 953
(2003) · PsycEXTRA Dataset · doi:10.1037/e310362005-001 · Cited by 6 · see citing works
International Journal of Cosmetic Science
(2010) · International Journal of Cosmetic Science · doi:10.1111/(issn)1468-2494 · Cited by 5 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Victor S Sheng, Foster Provost, and Panagiotis G Ipeirotis. 2008. Get another label? improving data quality and data mining using multiple, noisy labelers. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 614-622.
Cited by
Sorted by citations
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., et al. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00371 · Cited by 11,953 · see citing works
“We repeat the analysis for perceived age in Table 2 (bottom left), noting that those who are perceived to be younger and older have been shown to be underrepresented in large-scale datasets [110].”
MethodAdvances, challenges and opportunities in creating data for trustworthy AI
Liang, W., Tadesse, G. A., Ho, D. E., Fei-Fei, L., Zaharia, M., Zhang, C., & Zou, J. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00516-1 · Cited by 582 · see citing works
Self-citationMachine learning for high-entropy alloys: Progress, challenges and opportunities
Liu, X., Zhang, J., & Pei, Z. (2022) · Progress in Materials Science · doi:10.1016/j.pmatsci.2022.101018 · Cited by 417 · see citing works
Intriguing Properties of Vision Transformers
Naseer, M., Ranasinghe, K., Khan, S., Hayat, M., Khan, F. S., & Yang, M.-H. (2021) · arXiv · doi:10.48550/arxiv.2105.10497 · Cited by 431 · see citing works
Multi-Modal Knowledge Graph Construction and Application: A Survey
Zhu, X., Li, Z., Wang, X., Jiang, X., Sun, P., Wang, X., Xiao, Y., & Yuan, N. J. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3224228 · Cited by 324 · see citing works
Participation Is not a Design Fix for Machine Learning
Sloane, M., Moss, E., Awomolo, O., & Forlano, L. (2022) · Equity and Access in Algorithms, Mechanisms, and Optimization · doi:10.1145/3551624.3555285 · Cited by 267 · see citing works
Large image datasets: A pyrrhic win for computer vision?
Birhane, A., & Prabhu, V. U. (2021) · 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv48630.2021.00158 · Cited by 257 · see citing works
Mapping value sensitive design onto AI for social good principles
Umbrello, S., & van de Poel, I. (2021) · AI and Ethics · doi:10.1007/s43681-021-00038-3 · Cited by 251 · see citing works
Kubric: A scalable dataset generator
Greff, K., Belletti, F., Beyer, L., Doersch, C., Du, Y., Duckworth, D., Fleet, D. J., Gnanapragasam, D., Golemo, F., Herrmann, C., et al. (2022) · 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/cvpr52688.2022.00373 · Cited by 235 · 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
Walmsley, M., Lintott, C. J., Géron, T., Kruk, S., Krawczyk, C., Willett, K., Bamford, S. P., Kelvin, L. S., Fortson, L., Gal, Y., et al. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab2093 · Cited by 198 · see citing works
A sociotechnical view of algorithmic fairness
Dolata, M., Feuerriegel, S., & Schwabe, G. (2021) · Information Systems Journal · doi:10.1111/isj.12370 · Cited by 177 · see citing works
Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases
Steed, R., & Caliskan, A. (2021) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3442188.3445932 · Cited by 167 · see citing works
Domain Generalization by Mutual-Information Regularization with Pre-trained Models
Cha, J., Lee, K., Park, S., & Chun, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20050-2_26 · Cited by 126 · see citing works
Fair Attribute Classification through Latent Space De-biasing
Ramaswamy, V., Kim, S. S. Y., & Russakovsky, O. (2021) · 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/cvpr46437.2021.00918 · Cited by 122 · see citing works
Self-citationAlgorithmic fairness datasets: the story so far
Fabris, A., Messina, S., Silvello, G., & Susto, G. A. (2022) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-022-00854-z · Cited by 128 · see citing works
Balayn, A., Lofi, C., & Houben, G.-J. (2021) · The VLDB Journal · doi:10.1007/s00778-021-00671-8 · Cited by 98 · see citing works
Hagendorff, T., Bossert, L. N., Tse, Y. F., & Singer, P. (2022) · AI and Ethics · doi:10.1007/s43681-022-00199-9 · Cited by 91 · see citing works
Understanding and Evaluating Racial Biases in Image Captioning
Zhao, D., Wang, A., & Russakovsky, O. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01456 · Cited by 84 · see citing works
Self-citationFairness issues, current approaches, and challenges in machine learning models
Jui, T. D., & Rivas, P. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-023-02083-2 · Cited by 91 · see citing works
Marabelli, M., & Newell, S. (2023) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2023.101774 · Cited by 80 · see citing works
REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets
Wang, A., Liu, A., Zhang, R., Kleiman, A., Kim, L., Zhao, D., Shirai, I., Narayanan, A., & Russakovsky, O. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01625-5 · Cited by 85 · see citing works
Self-citationTowards Measuring Fairness in AI: The Casual Conversations Dataset
Hazirbas, C., Bitton, J., Dolhansky, B., Pan, J., Gordo, A., & Ferrer, C. C. (2022) · IEEE Transactions on Biometrics, Behavior, and Identity Science · doi:10.1109/tbiom.2021.3132237 · Cited by 79 · see citing works
REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets
Wang, A., Narayanan, A., & Russakovsky, O. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58580-8_43 · Cited by 72 · see citing works
Self-citationYücer, Ş., Akçay, S., Al-Moubayed, N., & Breckon, T. P. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00017 · Cited by 70 · see citing works
Pre-training Without Natural Images
Kataoka, H., Okayasu, K., Matsumoto, A., Yamagata, E., Yamada, R., Inoue, N., Nakamura, A., & Satoh, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69544-6_35 · Cited by 70 · see citing works
Protein language models are biased by unequal sequence sampling across the tree of life
Ding, F., & Steinhardt, J. (2024) · doi:10.1101/2024.03.07.584001 · Cited by 57 · see citing works
“They only care to show us the wheelchair”: disability representation in text-to-image AI models
Mack, K. A., Qadri, R., Denton, R., Kane, S. K., & Bennett, C. L. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642166 · Cited by 67 · see citing works
Ethical AI in facial expression analysis: racial bias
Sham, A. H., Aktas, K., Rizhinashvili, D., Kuklyanov, D., Alisinanoğlu, F., Ofodile, I., Ozcinar, C., & Anbarjafari, G. (2022) · Signal Image and Video Processing · doi:10.1007/s11760-022-02246-8 · Cited by 65 · see citing works
Practical galaxy morphology tools from deep supervised representation learning
Walmsley, M., Scaife, A. M. M., Lintott, C. J., Lochner, M., Etsebeth, V., Géron, T., Dickinson, H. J., Fortson, L., Kruk, S., Masters, K. L., et al. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac525 · Cited by 66 · see citing works
Schramowski, P., Tauchmann, C., & Kersting, K. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533192 · Cited by 63 · see citing works
WEIRD FAccTs: How Western, Educated, Industrialized, Rich, and Democratic is FAccT?
Septiandri, A. A., Constantinides, M., Tahaei, M., & Quercia, D. (2023) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3593013.3593985 · Cited by 58 · see citing works
Risk of Training Diagnostic Algorithms on Data with Demographic Bias
Abbasi-Sureshjani, S., Raumanns, R., Michels, B. E. J., Schouten, G., & Cheplygina, V. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61166-8_20 · Cited by 54 · see citing works
Pre-Training Without Natural Images
Kataoka, H., Okayasu, K., Matsumoto, A., Yamagata, E., Yamada, R., Inoue, N., Nakamura, A., & Satoh, Y. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-021-01555-8 · Cited by 51 · see citing works
Laufer, B., Jain, S., Cooper, A. F., Kleinberg, J. M., & Heidari, H. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533107 · Cited by 54 · see citing works
Quantifying Societal Bias Amplification in Image Captioning
Hirota, Y., Nakashima, Y., & García, N. (2022) · 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/cvpr52688.2022.01309 · Cited by 50 · see citing works
Machine learning in geography–Past, present, and future
Lavallin, A. V., & Downs, J. A. (2021) · Geography Compass · doi:10.1111/gec3.12563 · Cited by 48 · see citing works
Deep learning in bioinformatics
Yousef, M., & Allmer, J. (2023) · TURKISH JOURNAL OF BIOLOGY · doi:10.55730/1300-0152.2671 · Cited by 57 · see citing works
Auditing and instructing text-to-image generation models on fairness
Friedrich, F., Brack, M., Struppek, L., Hintersdorf, D., Schramowski, P., Luccioni, A. S., & Kersting, K. (2024) · AI and Ethics · doi:10.1007/s43681-024-00531-5 · Cited by 47 · see citing works
Mittal, S., Thakral, K., Singh, R., Mayank Vatsa, A., Glaser, T., Ferrer, C. C., & Hassner, T. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00874-y · Cited by 53 · see citing works
Uncurated Image-Text Datasets: Shedding Light on Demographic Bias
García, N., Hirota, Y., Wu, Y., & Nakashima, Y. (2023) · 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/cvpr52729.2023.00672 · Cited by 45 · see citing works
Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent Circumvention
Whitney, C. D., & Norman, J. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3659002 · Cited by 47 · see citing works
Synthetic Object Recognition Dataset for Industries
Akar, C. A., Tekli, J., Jess, D., Khoury, M., Kamradt, M., & Guthe, M. (2022) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi55357.2022.9991784 · Cited by 42 · see citing works
Measuring Representational Harms in Image Captioning
Wang, A., Barocas, S., Laird, K., & Wallach, H. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533099 · Cited by 40 · see citing works
A systematic review of open data in agriculture
Chamorro-Padial, J., Garcı́a, R., & Gil, R. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.108775 · Cited by 40 · see citing works
Balayn, A., Yurrita, M., Yang, J., & Gadiraju, U. (2023) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3600211.3604674 · Cited by 40 · see citing works
Data Representativeness in Accessibility Datasets: A Meta-Analysis
Kamikubo, R., Wang, L., Marte, C., Mahmood, A., & Kacorri, H. (2022) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3517428.3544826 · Cited by 41 · see citing works
Computer Vision and Conflicting Values: Describing People with Automated Alt Text
Hanley, M., Barocas, S., Levy, K., Azenkot, S., & Nissenbaum, H. (2021) · Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3461702.3462620 · Cited by 38 · see citing works
FACET: Fairness in Computer Vision Evaluation Benchmark
Gustafson, L., Rolland, C., Ravi, N., Duval, Q., Adcock, A., Fu, C.-Y., Hall, M. A., & Ross, C. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01863 · Cited by 37 · see citing works
Can Vision Transformers Learn without Natural Images?
Nakashima, K., Kataoka, H., Matsumoto, A., Iwata, K., Inoue, N., & Satoh, Y. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20094 · Cited by 35 · see citing works
FairGRAPE: Fairness-Aware GRAdient Pruning mEthod for Face Attribute Classification
Lin, X., Kim, S., & Joo, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19778-9_24 · Cited by 35 · see citing works
A Step Toward More Inclusive People Annotations for Fairness
Schumann, C., Ricco, S., Prabhu, U., Ferrari, V., & Pantofaru, C. (2021) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3461702.3462594 · Cited by 34 · see citing works
Replacing Labeled Real-image Datasets with Auto-generated Contours
Kataoka, H., Hayamizu, R., Yamada, R., Nakashima, K., Takashima, S., Zhang, X., Martinez‐Noriega, E. J., Inoue, N., & Yokota, R. (2022) · 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/cvpr52688.2022.02055 · Cited by 33 · see citing works
Concept Generalization in Visual Representation Learning
Sarıyıldız, M. B., Kalantidis, Y., Larlus, D., & Alahari, K. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00949 · Cited by 33 · 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
A Dense Material Segmentation Dataset for Indoor and Outdoor Scene Parsing
Upchurch, P., & Niu, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20074-8_26 · Cited by 31 · see citing works
Bugs in the Data: How ImageNet Misrepresents Biodiversity
Luccioni, A. S., & Rolnick, D. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i12.26682 · Cited by 31 · see citing works
Wu, B., Shi, Z., Zheng, H., Peng, M., & Meng, S. (2024) · Bulletin of Engineering Geology and the Environment · doi:10.1007/s10064-024-03980-8 · Cited by 29 · see citing works
Lu, M., Zhao, Q., Zhang, J., Pohl, K. M., Fei-Fei, L., Niebles, J. C., & Adeli, E. (2021) · 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/cvpr46437.2021.01077 · Cited by 30 · see citing works
Self-citationHe, K., Mao, R., Huang, Y., Gong, T., Li, C., & Cambria, E. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3314807 · Cited by 31 · see citing works
Algorithmic Agency and Autonomy in Archaeological Practice
Huggett, J. (2021) · Open Archaeology · doi:10.1515/opar-2020-0136 · Cited by 28 · see citing works
maleve, N. (2020) · AI & Society · doi:10.1007/s00146-020-01093-w · Cited by 27 · see citing works
Open-domain Visual Entity Recognition: Towards Recognizing Millions of Wikipedia Entities
Hu, H., Luan, Y., Chen, Y., Khandelwal, U., Joshi, M., Lee, K., Toutanova, K., & Chang, M.-W. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01108 · Cited by 27 · see citing works
The agency of computer vision models as optical instruments
Smits, T., & Wevers, M. (2021) · Visual Communication · doi:10.1177/1470357221992097 · Cited by 26 · see citing works
Adomavicius, G., & Yang, M. (2022) · ACM Transactions on Management Information Systems · doi:10.1145/3519420 · Cited by 20 · see citing works
Ilias Leontiadis, A., Laskaridis, S., Venieris, S. I., & Lane, N. D. (2021) · IEEE Workshop on Mobile Computing Systems and Applications (WMCSA) · doi:10.1145/3446382.3448359 · Cited by 26 · see citing works
Searching for Computer Vision North Stars
Fei-Fei, L., & Krishna, R. (2022) · Daedalus · doi:10.1162/daed_a_01902 · Cited by 25 · see citing works
Self-citationVisual Atoms: Pre-Training Vision Transformers with Sinusoidal Waves
Takashima, S., Hayamizu, R., Inoue, N., Kataoka, H., & Yokota, R. (2023) · 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/cvpr52729.2023.01782 · Cited by 25 · see citing works
Enhancing Fairness in Face Detection in Computer Vision Systems by Demographic Bias Mitigation
Yang, Y., Gupta, A., Feng, J., Singhal, P., Yadav, V., Wu, Y., Natarajan, P., Hedau, V., & Joo, J. (2022) · Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3514094.3534153 · Cited by 24 · see citing works
Shapes and frictions of synthetic data
Offenhuber, D. (2024) · Big Data & Society · doi:10.1177/20539517241249390 · Cited by 24 · see citing works
Krook, J., Winter, P., Downer, J., & Blockx, J. (2025) · AI and Ethics · doi:10.1007/s43681-025-00674-z · Cited by 32 · see citing works
Fairness by “Where”: A Statistically-Robust and Model-Agnostic Bi-level Learning Framework
Xie, Y., He, E., Jia, X., Chen, W., Skakun, S. V., Bao, H., Jiang, Z., Ghosh, R., & Ravirathinam, P. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i11.21481 · Cited by 24 · see citing works
Gender Artifacts in Visual Datasets
Meister, N., Zhao, D., Wang, A., Ramaswamy, V., Fong, R., & Russakovsky, O. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00446 · Cited by 26 · see citing works
Self-citationStroke Lesion Segmentation and Deep Learning: A Comprehensive Review
Malik, M., Chong, B., Fernandez, J., Shim, V., Kasabov, N. K., & Wang, A. (2024) · Bioengineering · doi:10.3390/bioengineering11010086 · Cited by 23 · see citing works
Beyond Algorithmic Bias: A Socio-Computational Interrogation of the Google Search by Image Algorithm
Papakyriakopoulos, O., & Mboya, A. M. (2022) · Social Science Computer Review · doi:10.1177/08944393211073169 · Cited by 23 · see citing works
Rehman, M. U., Shafique, A., Khan, K. H., Khalid, S., Alotaibi, A. A., Althobaiti, T., Ramzan, N., Ahmad, J. E., Shah, S. A., & Abbasi, Q. H. (2022) · Sensors · doi:10.3390/s22020461 · Cited by 24 · see citing works
A Framework for Deprecating Datasets: Standardizing Documentation, Identification, and Communication
Luccioni, A. S., Corry, F., Sridharan, H., Ananny, M., Schultz, J., & Crawford, K. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533086 · Cited by 22 · see citing works
Automatic Detection of Photovoltaic Farms Using Satellite Imagery and Convolutional Neural Networks
Ιωάννου, Κ., & Myronidis, D. (2021) · Sustainability · doi:10.3390/su13095323 · Cited by 21 · see citing works
A study on the distribution of social biases in self-supervised learning visual models
Sirotkin, K., Carballeira, P., & Escudero-Viñolo, M. (2022) · 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/cvpr52688.2022.01019 · Cited by 21 · see citing works
Fan Fault Diagnosis Using Acoustic Emission and Deep Learning Methods
Ciaburro, G., Padmanabhan, S., Maleh, Y., & Puyana‐Romero, V. (2023) · Informatics · doi:10.3390/informatics10010024 · Cited by 21 · see citing works
A Survey on Data Quality Dimensions and Tools for Machine Learning Invited Paper
Zhou, Y., Tu, F., Sha, K., Ding, J., & Chen, H. (2024) · IEEE International Conference on Artificial Intelligence Testing (AITest) · doi:10.1109/aitest62860.2024.00023 · Cited by 21 · see citing works
Impact of Annotator Demographics on Sentiment Dataset Labeling
Ding, Y., You, J., Machulla, T., Jacobs, J., Sen, P., & Höllerer, T. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555632 · Cited by 20 · see citing works
Benchmarking Omni-Vision Representation Through the Lens of Visual Realms
Zhang, Y., Yin, Z., Shao, J., & Liu, Z. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20071-7_35 · Cited by 19 · see citing works
Black, E., Naidu, R. C. A., Ghani, R., Rodolfa, K. T., Ho, D. E., & Heidari, H. (2023) · ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) · doi:10.1145/3617694.3623259 · Cited by 19 · see citing works
Vision Intelligence for Smart Sheep Farming: Applying Ensemble Learning to Detect Sheep Breeds
Himel, G. M. S., Islam, M. M., & Rahaman, M. (2023) · Artificial Intelligence in Agriculture · doi:10.1016/j.aiia.2023.11.002 · Cited by 18 · see citing works
Panch, T., Pollard, T., Mattie, H., Lindemer, E., Keane, P. A., & Celi, L. A. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0295-6 · Cited by 18 · see citing works
Measuring User Experience Inclusivity in Human-AI Interaction via Five User Problem-Solving Styles
Anderson, A. W., Guevara, J. N., Moussaoui, F., Li, T., Vorvoreanu, M., & Burnett, M. (2024) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3663740 · Cited by 17 · see citing works
Fairness Indicators for Systematic Assessments of Visual Feature Extractors
Goyal, P., Romero, A., Hazırbaş, C., Sagun, L., & Usunier, N. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533074 · Cited by 17 · see citing works
Liu, F., Zhang, Z., & Li, S. (2023) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2023.3332075 · Cited by 17 · see citing works
Crowdsourcing Detection of Sampling Biases in Image Datasets
Hu, X., Wang, H., Vegesana, A., Dube, S., Yu, K., Kao, G., Chen, S.-H., Lu, Y.-H., Thiruvathukal, G. K., & Yin, M. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366423.3380063 · Cited by 17 · see citing works
Gender Bias in Natural Language Processing and Computer Vision: A Comparative Survey
Bartl, M., Mandal, A., Leavy, S., & Little, S. (2024) · ACM Computing Surveys · doi:10.1145/3700438 · Cited by 16 · see citing works
Physics Guided Neural Networks for Time-Aware Fairness: An Application in Crop Yield Prediction
He, E., Xie, Y., Liu, L., Chen, W., Jin, Z., & Jia, X. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i12.26664 · Cited by 16 · see citing works
Fairness and Bias in Robot Learning
Londoño, L., Hurtado, J. V., Hertz, N., Kellmeyer, P., Vöeneky, S., & Valada, A. (2024) · Proceedings of the IEEE · doi:10.1109/jproc.2024.3403898 · Cited by 31 · see citing works
Streinz, T. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108954006.010 · Cited by 15 · see citing works
First- and Second-Level Bias in Automated Decision-making
Franke, U. (2022) · Philosophy & Technology · doi:10.1007/s13347-022-00500-y · Cited by 16 · see citing works
Mawgoud, A. A., Taha, M. H. N., Abu-Talleb, A., & Kotb, A. M. (2022) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-022-00339-w · Cited by 15 · see citing works
Casual Conversations: A dataset for measuring fairness in AI
Hazirbas, C., Bitton, J., Dolhansky, B., Pan, J., Gordo, A., & Ferrer, C. C. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00258 · Cited by 15 · see citing works
Formula-driven Supervised Learning with Recursive Tiling Patterns
Kataoka, H., Matsumoto, A., Yamada, R., Satoh, Y., Yamagata, E., & Inoue, N. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00455 · Cited by 14 · see citing works
Gupta, R. K., & Sinha, R. (2023) · IEEE Access · doi:10.1109/access.2023.3297091 · Cited by 14 · see citing works
Wang, C., & Li, X. (2023) · Journal of Atmospheric and Oceanic Technology · doi:10.1175/jtech-d-23-0026.1 · Cited by 14 · see citing works
- Authors
- Yang, K., Qinami, K., Fei-Fei, L., Deng, J., & Russakovsky, O.
- Affiliations
- Princeton University (US)
- Stanford University (US)
- Venue
- Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency
- Publisher
- ACM
- Published
- 2020-01-27
- Pages
- 547-558
- OpenAlex
- W4288083803
- Cited by
- 254
- References
- 50
- Citations by year
- 2026: 13 · 2025: 37 · 2024: 47 · 2023: 48 · 2022: 58 · 2021: 42 · 2020: 9
- FWCI
- 40.71
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Center for Hierarchical Manufacturing, National Science Foundation (1763642)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- ImageNet, underrepresented groups, WordNet, label distribution, data bias
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
