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Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure
9 references·16 citing·30 second hop
Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure (2019)Uncovering and Mitigating Algorithmic Bia…Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification (2018)Gender Shades: Intersectional…Bias detectives: the researchers striving to make algorithms fair (2018)Bias detectives: the research…Semantics derived automatically from language corpora contain human-like biases (2017)Semantics derived automatical…Optimized Pre-Processing for Discrimination Prevention (2017)Optimized Pre-Processing for …Avoiding Discrimination through Causal Reasoning (2017)Avoiding Discrimination throu…Man is to Computer Programmer as Woman is to Homemaker? 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References

  • 2018Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification3,509 cited
  • 2017Semantics derived automatically from language corpora contain human-like biases2,845 cited
  • 2016Equality of Opportunity in Supervised Learning2,535 cited
  • 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings1,615 cited
  • 2013Learning Fair Representations1,126 cited
  • 2010Consumer credit-risk models via machine-learning algorithms861 cited
  • 2017Optimized Pre-Processing for Discrimination Prevention459 cited
  • 2017Avoiding Discrimination through Causal Reasoning358 cited
  • 2018Bias detectives: the researchers striving to make algorithms fair205 cited

Cited by

  • 2020Investigating Bias and Fairness in Facial Expression Recognition174 cited
  • 2023AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications155 cited
  • 2021Understanding and Mitigating Annotation Bias in Facial Expression Recognition120 cited
  • 2022Algorithmic fairness datasets: the story so far114 cited
  • 2022Algorithmic Political Bias in Artificial Intelligence Systems111 cited
  • 2021Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and unfairness within data management and analytics systems

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97 cited
  • 2023Age-related bias and artificial intelligence: a scoping review96 cited
  • 2023Disentangling Fairness Perceptions in Algorithmic Decision-Making: the Effects of Explanations, Human Oversight, and Contestability78 cited
  • 2022NeuronFair63 cited
  • 2021Deep Entity Matching63 cited
  • 2022Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes62 cited
  • 2020Bias and ethical considerations in machine learning and the automation of perioperative risk assessment59 cited
  • 2023Fairness in Deep Learning: A Survey on Vision and Language Research57 cited
  • 2019Fair Generative Modeling via Weak Supervision52 cited
  • 2022Racial, skin tone, and sex disparities in automated proctoring software49 cited
  • 2022We have to talk about emotional AI and crime42 cited