The DOI matches its Crossref record; 45 of the 48 references it cites were found in a registry or an index.
Women Also Snowboard: Overcoming Bias in Captioning Models
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In the citing paper
“Likewise, a related objective of XAI is highlighting bias in the data a model was exposed to [168, 169].”
From Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI (2020) · cites this paper as [168]
In the citing paper
“It is well acknowledged that machine learning models, such as those ofered by the API providers, are prone to exhibiting various kinds of discriminatory behaviours, including diferent error rates across demographic groups [22,126,127], stereotyping [64,163] or minority groups under-representation [134,137].”
From Out of Context: Investigating the Bias and Fairness Concerns of “Artificial Intelligence as a Service” (2023) · cites this paper as [64]
Citations
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“One common theme is the notion of bias amplification, in which bias is not only learned, but amplified (Bolukbasi et al., 2016; Zhao et al., 2017).”
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Top-Down Neural Attention by Excitation Backprop
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“concerned about whether contextual clues in complex scenes bias the production of gendered words during sentence generation. Gender classification has also been studied in natural language processing ([41,42], [43]). Ethical Considerations. Frequently, gender classification is seen as a binary task: data points are labe…”
BackgroundReporting bias and knowledge acquisition
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Cited by
Sorted by citations
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
CLIPScore: A Reference-free Evaluation Metric for Image Captioning
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Language (Technology) is Power: A Critical Survey of “Bias” in NLP
Blodgett, S. L., Barocas, S., Daumé III, H., & Wallach, H. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.485 · Cited by 1,050
Flamingo: a visual language model for few-shot learning
Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1723 · Cited by 966
“Its ability to additionally handle visual inputs poses specific risks such as gender and racial biases relating to the contents of the input images, similar to a number of visual recognition systems [12, 21, 37, 97, 147].”
BackgroundUnbiased Scene Graph Generation From Biased Training
Tang, K., Niu, Y., Huang, J., Shi, J., & Zhang, H. (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/cvpr42600.2020.00377 · Cited by 810
Metadata
- Authors
- Hendricks, L. A., Burns, K., Saenko, K., Darrell, T., & Rohrbach, A.
- Affiliations
- University of California, Berkeley (US)
- Boston University (US)
- Corresponding author
- Hendricks, Lisa Anne
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 793-811
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2963349562
- Cited by
- 404
- References
- 48
- Citations by year
- 2026: 14 · 2025: 22 · 2024: 48 · 2023: 56 · 2022: 75 · 2021: 80 · 2020: 73 · 2019: 34 · 2018: 2
- FWCI
- 84.59
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- image captioning, gender bias, bias mitigation, women in media
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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.
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