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Women Also Snowboard: Overcoming Bias in Captioning Models
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“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]
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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
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 ›
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