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Learning from Failure: Training Debiased Classifier from Biased Classifier
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Learning from Failure: Training Debiased Classifier from Biased Classifier (2020)Learning from Failure: Training Debiased …A Survey on Bias and Fairness in Machine Learning (2021)A Survey on Bias and Fairness…REPAIR: Removing Representation Bias by Dataset Resampling (2019)REPAIR: Removing Representati…The Filter Bubble: What the Internet Is Hiding from You (2011)The Filter Bubble: What the I…Revisiting the Importance of Amplifying Bias for Debiasing (2023)Revisiting the Importance of …Overwriting Pretrained Bias with Finetuning Data (2023)Overwriting Pretrained Bias w…Unsupervised Learning of Debiased Representations with Pseudo-Attributes (2022)Unsupervised Learning of Debi…An Investigation of Critical Issues in Bias Mitigation Techniques (2022)An Investigation of Critical …Learning Bias-Invariant Representation by Cross-Sample Mutual Information Minimization (2021)Learning Bias-Invariant Repre…Fairness through awareness (2012)Semantics derived automatically from language corpora contain human-like biases (2017)Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification (2018)Algorithmic Bias in Education (2021)Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation (2023)Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies (2023)Certifying and Removing Disparate Impact (2015)Equality of Opportunity in Supervised Learning (2016)Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings (2016)Climate Change, Environmental Disasters, and Health Inequities: The Underlying Role of Structural Inequalities (2022)Representation Bias in Data: A Survey on Identification and Resolution Techniques (2023)Comprehensive study of driver behavior monitoring systems using computer vision and machine learning techniques (2024)Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems (2010)Big Data's Disparate Impact (2014)Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments (2017)Cognitive Bias Mitigation in Executive Decision-Making: A Data-Driven Approach Integrating Big Data Analytics, AI, and Explainable Systems (2025)Fairness Beyond Disparate Treatment & Disparate Impact (2017)Mitigating Unwanted Biases with Adversarial Learning (2018)Image fairness in deep learning: problems, models, and challenges (2022)Robust Solutions of Optimization Problems Affected by Uncertain Probabilities (2012)Learning Fair Representations (2013)Women Also Snowboard: Overcoming Bias in Captioning Models (2018)BiasAdv: Bias-Adversarial Augmentation for Model Debiasing (2023)EnD: Entangling and Disentangling deep representations for bias correction (2021)FLAC: Fairness-Aware Representation Learning by Suppressing Attribute-Class Associations (2024)Model Cards for Model Reporting (2019)Exposure to ideologically diverse news and opinion on Facebook (2015)Filter Bubbles, Echo Chambers, and Online News Consumption (2016)Beyond misinformation: Understanding and coping with the “post-truth” era. (2017)
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References

  • 2021A Survey on Bias and Fairness in Machine Learning5,798 cited
  • 2011The Filter Bubble: What the Internet Is Hiding from You4,429 cited
  • 2019REPAIR: Removing Representation Bias by Dataset Resampling228 cited

Cited by

  • 2021Learning Bias-Invariant Representation by Cross-Sample Mutual Information Minimization29 cited
  • 2022Unsupervised Learning of Debiased Representations with Pseudo-Attributes28 cited
  • 2023Overwriting Pretrained Bias with Finetuning Data24 cited
  • 2022An Investigation of Critical Issues in Bias Mitigation Techniques22 cited
  • 2023Revisiting the Importance of Amplifying Bias for Debiasing19 cited

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