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Evaluating Fairness Strategies in Educational Data Mining: A Comparative Study of Bias Mitigation Techniques
13 references·0 citing·37 second hop
Evaluating Fairness Strategies in Educational Data Mining: A Comparative Study of Bias Mitigation Techniques (2025)Evaluating Fairness Strategies in Educati…Debiasing Education Algorithms (2024)Debiasing Education AlgorithmsMoral Machines or Tyranny of the Majority? A Systematic Review on Predictive Bias in Education (2023)Moral Machines or Tyranny of …Fairness in Machine Learning: A Survey (2023)Fairness in Machine Learning:…The statistical fairness field guide: perspectives from social and formal sciences (2022)The statistical fairness fiel…Experts’ View on Challenges and Needs for Fairness in Artificial Intelligence for Education (2022)Experts’ View on Challenges a…Algorithmic Bias in Education (2021)Algorithmic Bias in EducationMitigating Unwanted Biases with Adversarial Learning (2018)Mitigating Unwanted Biases wi…On Fairness and Calibration (2017)On Fairness and CalibrationEquality of Opportunity in Supervised Learning (2016)Equality of Opportunity in Su…Certifying and Removing Disparate Impact (2015)Certifying and Removing Dispa…Learning Fair Representations (2013)Learning Fair RepresentationsFairness-Aware Classifier with Prejudice Remover Regularizer (2012)Fairness-Aware Classifier wit…Data preprocessing techniques for classification without discrimination (2011)Data preprocessing techniques…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)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)Big Data's Disparate Impact (2014)Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments (2017)ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope (2023)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)CRITICAL QUESTIONS FOR BIG DATA (2012)Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification (2018)A Survey on Bias and Fairness in Machine Learning (2021)An Overview of Artificial Intelligence Ethics (2022)Trustworthy AI: From Principles to Practices (2022)Considerations for addressing bias in artificial intelligence for health equity (2023)Mapping the Margins: Intersectionality, Identity Politics, and Violence against Women of Color (1991)Dissecting racial bias in an algorithm used to manage the health of populations (2019)The Ethics of Artificial Intelligence in Education (2022)Fairness, Accountability, Transparency, and Ethics (FATE) in Artificial Intelligence (AI) and higher education: A systematic review (2023)8 Decolonizing Methodologies: Research and Indigenous Peoples (2024)The impact of generative artificial intelligence on socioeconomic inequalities and policy making (2024)Developing a framework for responsible innovation (2013)The PRISMA 2020 statement: An updated guideline for reporting systematic reviews (2021)Algorithmic fairness in predictive policing (2024)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)Algorithmic Decision Making and the Cost of Fairness (2017)AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias (2018)The Measure and Mismeasure of Fairness (2018)Mitigating Gender Bias in Natural Language Processing: Literature Review (2019)
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References

  • 2016Equality of Opportunity in Supervised Learning2,535 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
  • 2013Learning Fair Representations1,126 cited
  • 2021Algorithmic Bias in Education988 cited
  • 2012Fairness-Aware Classifier with Prejudice Remover Regularizer694 cited
  • 2023Fairness in Machine Learning: A Survey645 cited
  • 2017On Fairness and Calibration509 cited
  • 2024Debiasing Education Algorithms57 cited
  • 2022The statistical fairness field guide: perspectives from social and formal sciences51 cited
  • 2023Moral Machines or Tyranny of the Majority? A Systematic Review on Predictive Bias in Education43 cited
  • 2022Experts’ View on Challenges and Needs for Fairness in Artificial Intelligence for Education36 cited

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