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A Survey on the Use of Synthetic Data for Enhancing Key Aspects of Trustworthy AI in the Energy Domain: Challenges and Opportunities
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References

  • 2021A Survey on Bias and Fairness in Machine Learning5,798 cited
  • 20161,500 scientists lift the lid on reproducibility4,200 cited
  • 2018Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification3,509 cited
  • 2012Fairness through awareness3,501 cited
  • 2016Equality of Opportunity in Supervised Learning2,535 cited
  • 2023ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope2,455 cited
  • 2015Certifying and Removing Disparate Impact1,827 cited
  • 2011Data preprocessing techniques for classification without discrimination1,304 cited
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  • 2019A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective977 cited
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  • 2021“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI774 cited
  • 2022Trustworthy Artificial Intelligence: A Review

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707 cited
  • 2019Actionable Auditing650 cited
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