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Auditing and Generating Synthetic Data with Controllable Trust Trade-offs
16 references·1 citing·43 second hop
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References

  • 2018Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification3,509 cited
  • 2021On the Opportunities and Risks of Foundation Models2,670 cited
  • 2019Model Cards for Model Reporting2,653 cited
  • 2007When Multiplication Doesn't Equal Quick Addition: Examining Intersectionality as a Research Paradigm1,984 cited
  • 2020Closing the AI accountability gap1,445 cited
  • 2018Mitigating Unwanted Biases with Adversarial Learning1,387 cited
  • 2018On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness1,361 cited
  • 2003Audit Cultures1,139 cited
  • 1999The Audit Society938 cited
  • 2014A data-driven approach to predict the success of bank telemarketing875 cited
  • 2017Optimized Pre-Processing for Discrimination Prevention459 cited
  • 2019FactSheets: Increasing trust in AI services through supplier's declarations of conformity434 cited
  • 2023Auditing large language models: a three-layered approach287 cited
  • 2018Why Is My Classifier Discriminatory?186 cited
  • 2021The Problem of Fairness in Synthetic Healthcare Data92 cited

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2022
The US Algorithmic Accountability Act of 2022 vs. The EU Artificial Intelligence Act: what can they learn from each other?
88 cited

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  • 2024SocialCounterfactuals: Probing and Mitigating Intersectional Social Biases in Vision-Language Models with Counterfactual Examples15 cited