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Causal Feature Selection for Algorithmic Fairness
13 references·1 citing·41 second hop
Causal Feature Selection for Algorithmic Fairness (2022)Causal Feature Selection for Algorithmic …Lessons from archives (2020)Lessons from archivesAlgorithmic Bias in Recidivism Prediction: A Causal Perspective (Student Abstract) (2020)Algorithmic Bias in Recidivis…Path-Specific Counterfactual Fairness (2019)Path-Specific Counterfactual …Interventional Fairness (2019)Interventional FairnessImproving Fairness in Machine Learning Systems (2019)Improving Fairness in Machine…Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality (2019)Fairness in Algorithmic Decis…Classification with Fairness Constraints (2019)Classification with Fairness …Open data integration (2018)Open data integrationFairness in Decision-Making — The Causal Explanation Formula (2018)Fairness in Decision-Making —…Conscientious Classification: A Data Scientist's Guide to Discrimination-Aware Classification (2017)Conscientious Classification:…Certifying and Removing Disparate Impact (2015)Certifying and Removing Dispa…Fairness-Aware Classifier with Prejudice Remover Regularizer (2012)Fairness-Aware Classifier wit…Data preprocessing techniques for classification without discrimination (2011)Data preprocessing techniques…On the relation of causality- versus correlation-based feature selection on model fairness (2024)On the relation of causality-…Discrimination-aware data mining (2008)Three naive Bayes approaches for discrimination-free classification (2010)Fairness through awareness (2012)Fair, Transparent, and Accountable Algorithmic Decision-making Processes (2017)Trustworthy Artificial Intelligence: A Review (2022)Fairness in Machine Learning: A Survey (2023)Uniform Guidelines on Employee Selection Procedures (2007)European Union Regulations on Algorithmic Decision Making and a “Right to Explanation” (2017)Semantics derived automatically from language corpora contain human-like biases (2017)A Survey on Bias and Fairness in Machine Learning (2021)Structural Equations with Latent Variables (1989)Structural Equations with Latent Variables. (1991)Fair preprocessing: towards understanding compositional fairness of data transformers in machine learning pipeline (2021)Multiobjective Optimization-Based Collective Opinion Generation With Fairness Concern (2023)Policy advice and best practices on bias and fairness in AI (2024)Equality of Opportunity in Supervised Learning (2016)Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments (2017)Fairness in rankings and recommendations: an overview (2021)Algorithmic discrimination in the credit domain: what do we know about it? (2023)Big Data's Disparate Impact (2014)An empirical characterization of fair machine learning for clinical risk prediction (2020)Fairness in credit scoring: Assessment, implementation and profit implications (2021)Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey (2023)Avoiding Discrimination through Causal Reasoning (2017)Counterfactual Fairness (2017)A clarification of the nuances in the fairness metrics landscape (2022)AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications (2023)Fairness and Abstraction in Sociotechnical Systems (2019)Model Cards for Model Reporting (2019)The global landscape of AI ethics guidelines (2019)Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development (2021)On the genealogy of machine learning datasets: A critical history of ImageNet (2021)Can HR adapt to the paradoxes of artificial intelligence? (2022)Fairness in Criminal Justice Risk Assessments: The State of the Art (2018)AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias (2019)Big Data and discrimination: perils, promises and solutions. A systematic review (2019)Integrating Ethics within Machine Learning Courses (2019)Addressing algorithmic bias and the perpetuation of health inequities: An AI bias aware framework (2022)Algorithmic bias: review, synthesis, and future research directions (2021)Taxonomy of Risks posed by Language Models (2022)Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies (2023)
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References

  • 2015Certifying and Removing Disparate Impact1,827 cited
  • 2011Data preprocessing techniques for classification without discrimination1,304 cited
  • 2019Improving Fairness in Machine Learning Systems776 cited
  • 2012Fairness-Aware Classifier with Prejudice Remover Regularizer694 cited
  • 2019Path-Specific Counterfactual Fairness246 cited
  • 2020Lessons from archives240 cited
  • 2019Classification with Fairness Constraints232 cited
  • 2018Fairness in Decision-Making — The Causal Explanation Formula202 cited
  • 2017Conscientious Classification: A Data Scientist's Guide to Discrimination-Aware Classification195 cited
  • 2019Interventional Fairness145 cited
  • 2018Open data integration88 cited
  • 2019Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality87 cited
  • 2020Algorithmic Bias in Recidivism Prediction: A Causal Perspective (Student Abstract)32 cited

Cited by

  • 2024On the relation of causality- versus correlation-based feature selection on model fairness22 cited

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