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Enhancing transparency and fairness in automated credit decisions: an explainable novel hybrid machine learning approach
13 references·1 citing·50 second hop
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References

  • 195317. A Value for n-Person Games6,944 cited
  • 2019Algorithmic Bias? An Empirical Study of Apparent Gender-Based Discrimination in the Display of STEM Career Ads854 cited
  • 2021CatBoost model and artificial intelligence techniques for corporate failure prediction397 cited
  • 2020A new deep learning ensemble credit risk evaluation model with an improved synthetic minority oversampling technique234 cited
  • 2021Bias Preservation in Machine Learning: The Legality of Fairness Metrics Under EU Non-Discrimination Law167 cited
  • 2018A hybrid deep learning model for consumer credit scoring75 cited
  • 2019Machine learning models and cost-sensitive decision trees for bond rating prediction66 cited
  • 2018Hybrid credit scoring model using neighborhood rough set and multi-layer ensemble classification59 cited
  • 2020A logistic regression model for consumer default risk57 cited
  • 2022Machine learning and artificial neural networks to construct P2P lending credit-scoring model: A case using Lending Club data52 cited
  • 2023Machine Learning for Enhanced Credit Risk Assessment: An Empirical Approach49 cited
  • 2020Bias in Machine Learning -- What is it Good for?48 cited
  • 2022Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets45 cited

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  • 2026

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When Life Gives You AI, Will You Turn It Into A Market for Lemons? Understanding How Information Asymmetries About AI System Capabilities Affect Market Outcomes and Adoption
2 cited