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Taxonomizing and Measuring Representational Harms: A Look at Image Tagging
16 references·3 citing·38 second hop
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References

  • 2018Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification3,509 cited
  • 2020Race after technology. Abolitionist tools for the new Jim Code1,870 cited
  • 2020Language (Technology) is Power: A Critical Survey of “Bias” in NLP1,044 cited
  • 2017Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints829 cited
  • 2015Unequal Representation and Gender Stereotypes in Image Search Results for Occupations509 cited
  • 2019Algorithms of oppression: How search engines reinforce racism459 cited
  • 2018Women Also Snowboard: Overcoming Bias in Captioning Models404 cited
  • 2021Measurement and Fairness362 cited
  • 2019Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations355 cited
  • 2020Towards a critical race methodology in algorithmic fairness295 cited
  • 2020Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation285 cited
  • 2021Excavating AI: the politics of images in machine learning training sets279 cited
  • 2021Large image datasets: A pyrrhic win for computer vision?258 cited
  • 2020Towards fairer datasets254 cited
  • 2019Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings

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225 cited
  • 2020Living Disability Theory: Reflections on Access, Research, and Design201 cited
  • Cited by

    • 2025Racial bias in AI-generated images28 cited
    • 2025Position is Power: System Prompts as a Mechanism of Bias in Large Language Models (LLMs)25 cited
    • 2024Benchmark suites instead of leaderboards for evaluating AI fairness17 cited