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Make your data fair: A survey of data preprocessing techniques that address biases in data towards fair AI
16 references·0 citing·37 second hop
Make your data fair: A survey of data preprocessing techniques that address biases in data towards fair AI (2024)Make your data fair: A survey of data pre…Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem (2022)Who Audits the Auditors? Reco…A Survey on Bias and Fairness in Machine Learning (2021)A Survey on Bias and Fairness…Fair Class Balancing: Enhancing Model Fairness without Observing Sensitive Attributes (2020)Fair Class Balancing: Enhanci…A snapshot of the frontiers of fairness in machine learning (2020)A snapshot of the frontiers o…Data Cleaning for Accurate, Fair, and Robust Models (2019)Data Cleaning for Accurate, F…Women Also Snowboard: Overcoming Bias in Captioning Models (2018)Women Also Snowboard: Overcom…The Price of Fair PCA: One Extra Dimension (2018)The Price of Fair PCA: One Ex…Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society (2018)Proceedings of the 2018 AAAI/…Mitigating Unwanted Biases with Adversarial Learning (2018)Mitigating Unwanted Biases wi…When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness (2017)When Worlds Collide: Integrat…Charter of Fundamental Rights of the European Union (2015)Charter of Fundamental Rights…Certifying and Removing Disparate Impact (2015)Certifying and Removing Dispa…Simpson's paradox in psychological science: a practical guide (2013)Simpson's paradox in psycholo…Fairness through awareness (2012)Fairness through awarenessData preprocessing techniques for classification without discrimination (2011)Data preprocessing techniques…Sex Bias in Graduate Admissions: Data from Berkeley (1975)Sex Bias in Graduate Admissio…Uniform Guidelines on Employee Selection Procedures (2007)European Union Regulations on Algorithmic Decision Making and a “Right to Explanation” (2017)On Simpson's Paradox and the Sure-Thing Principle (1972)Short and Long Sleep and Sleeping Pills (1979)How Retirement Saving Programs Increase Saving (1996)A Review on Fairness in Machine Learning (2022)Incorporating Fairness into Game Theory and Economics (2011)How the machine ‘thinks’: Understanding opacity in machine learning algorithms (2016)Princeton University Press (2017)Latent Class Cluster Analysis (2002)Investigating population heterogeneity with factor mixture models. (2005)Development and Distortion of Malaysian Public- Private Partnerships: Patronage, Privatised Profits and Pitfalls DOI: 10.1111/j.1467- 8500.2009.00655.x (2010)Mental disorders as networks of problems: a review of recent insights (2016)Maintenance, reserve and compensation: the cognitive neuroscience of healthy ageing (2018)Equality of Opportunity in Supervised Learning (2016)Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings (2016)Ensuring Fairness in Machine Learning to Advance Health Equity (2018)Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020)Addressing bias in big data and AI for health care: A call for open science (2021)Big Data's Disparate Impact (2014)Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations (2019)Towards fairer datasets (2020)Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation (2020)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)Counterfactual Fairness (2017)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)What Does It Mean to ‘Solve’ the Problem of Discrimination in Hiring? (2019)A Preliminary Evaluation of the Public Risk Perception Related to the COVID-19 Health Emergency in Italy (2020)A systematic review of artificial intelligence impact assessments (2023)Algorithmic bias: Senses, sources, solutions (2021)Algorithmic bias in machine learning-based marketing models (2022)A framework for AI-powered service innovation capability: Review and agenda for future research (2023)
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References

  • 2021A Survey on Bias and Fairness in Machine Learning5,798 cited
  • 2012Fairness through awareness3,501 cited
  • 2015Certifying and Removing Disparate Impact1,827 cited
  • 2018Mitigating Unwanted Biases with Adversarial Learning1,387 cited
  • 2011Data preprocessing techniques for classification without discrimination1,304 cited
  • 2015Charter of Fundamental Rights of the European Union702 cited
  • 1975Sex Bias in Graduate Admissions: Data from Berkeley659 cited
  • 2013Simpson's paradox in psychological science: a practical guide539 cited
  • 2018Women Also Snowboard: Overcoming Bias in Captioning Models404 cited
  • 2020A snapshot of the frontiers of fairness in machine learning355 cited
  • 2018Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society201 cited
  • 2022Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem195 cited
  • 2017When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness129 cited
  • 2020Fair Class Balancing: Enhancing Model Fairness without Observing Sensitive Attributes96 cited
  • 2018The Price of Fair PCA: One Extra Dimension84 cited

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  • 2019Data Cleaning for Accurate, Fair, and Robust Models62 cited
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