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Themis-ml: A Fairness-Aware Machine Learning Interface for End-To-End Discrimination Discovery and Mitigation
4 references·16 citing·40 second hopsampled
Themis-ml: A Fairness-Aware Machine Learning Interface for End-To-End Discrimination Discovery and Mitigation (2018)Themis-ml: A Fairness-Aware Machine Learn…Fairness-Aware Classifier with Prejudice Remover Regularizer (2012)Fairness-Aware Classifier wit…Fairness through awareness (2012)Fairness through awarenessDecision Theory for Discrimination-Aware Classification (2012)Decision Theory for Discrimin…Data preprocessing techniques for classification without discrimination (2011)Data preprocessing techniques…Towards Fair AI: Mitigating Bias in Credit Decisions—A Systematic Literature Review (2025)Towards Fair AI: Mitigating B…Software Fairness: An Analysis and Survey (2025)Software Fairness: An Analysi…Is artificial intelligence (AI) research biased and conceptually vague? A systematic review of research on bias and discrimination in the context of using AI in human resource management (2025)Is artificial intelligence (A…Trustworthy Artificial Intelligence in the Energy Sector: Landscape Analysis and Evaluation Framework (2024)Trustworthy Artificial Intell…Responsible artificial intelligence in human resources management: a review of the empirical literature (2023)Responsible artificial intell…Fairness-aware machine learning engineering: how far are we? (2023)Fairness-aware machine learni…A seven-layer model with checklists for standardising fairness assessment throughout the AI lifecycle (2023)A seven-layer model with chec…“☑ Fairness Toolkits, A Checkbox Culture?” On the Factors that Fragment Developer Practices in Handling Algorithmic Harms (2023)“☑ Fairness Toolkits, A Check…Towards model-based bias mitigation in machine learning (2022)Towards model-based bias miti…Dbias: detecting biases and ensuring fairness in news articles (2022)Dbias: detecting biases and e…Dbias: Detecting biases and ensuring Fairness in news articles (2022)Dbias: Detecting biases and e…Bias and fairness in machine learning and artificial intelligence (2022)Bias and fairness in machine …Missing the missing values: The ugly duckling of fairness in machine learning (2021)Missing the missing values: T…Mitigating Cognitive Biases in Machine Learning Algorithms for Decision Making (2020)Mitigating Cognitive Biases i…LiFT (2020)LiFTAI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias (2019)AI Fairness 360: An extensibl…Three naive Bayes approaches for discrimination-free classification (2010)The Filter Bubble: What the Internet Is Hiding from You (2011)Fairness and Abstraction in Sociotechnical Systems (2019)Discrimination-aware data mining (2008)Fairness in Machine Learning (2020)Fairness in Machine Learning: A Survey (2023)Justice as Fairness: A Restatement (2002)Incorporating Fairness into Game Theory and Economics (2011)How the machine ‘thinks’: Understanding opacity in machine learning algorithms (2016)Princeton University Press (2017)Classifying without discriminating (2009)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)Inherent Trade-Offs in the Fair Determination of Risk Scores (2016)Addressing bias in big data and AI for health care: A call for open science (2021)Algorithmic Bias in Education (2021)Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments (2017)Model Cards for Model Reporting (2019)Fairness in Deep Learning: A Survey on Vision and Language Research (2023)Establishing and evaluating trustworthy AI: overview and research challenges (2024)Confirmation Bias: A Ubiquitous Phenomenon in Many Guises (1998)Handbook of social psychology (2004)A Review on Fairness in Machine Learning (2022)Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation (2023)Dissecting racial bias in an algorithm used to manage the health of populations (2019)RoBERTa: A Robustly Optimized BERT Pretraining Approach (2019)A Survey on Bias and Fairness in Machine Learning (2021)FAIR Enough: Develop and Assess a FAIR-Compliant Dataset for Large Language Model Training? (2024)Defining quality in peer review reports: a scoping review (2025)Fairness-aware practices from developers’ perspective: A survey (2025)Research Commentary: Desperately Seeking the “IT” in IT Research—A Call to Theorizing the IT Artifact (2001)Big Data, new epistemologies and paradigm shifts (2014)The PRISMA 2020 statement: an updated guideline for reporting systematic reviews (2021)Revisiting the role of HR in the age of AI: bringing humans and machines closer together in the workplace (2024)The effects of artificial intelligence on human resource activities and the roles of the human resource triad: opportunities and challenges (2024)The Impact of Artificial Intelligence (AI) on Human Resource Management Practices (2024)Semantics derived automatically from language corpora contain human-like biases (2017)Artificial intelligence in practice: Opportunities, challenges, and ethical considerations. (2024)Data Quality in the Age of AI: A Review of Governance, Ethics, and the FAIR Principles (2025)
this work references cited by second hop· second hop is a sample of the most-cited connections

References

  • 2012Fairness through awareness3,545 cited
  • 2011Data preprocessing techniques for classification without discrimination1,344 cited
  • 2012Fairness-Aware Classifier with Prejudice Remover Regularizer694 cited
  • 2012Decision Theory for Discrimination-Aware Classification300 cited

Cited by

  • 2019AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias1,581 cited
  • 2023Responsible artificial intelligence in human resources management: a review of the empirical literature114 cited
  • 2021Missing the missing values: The ugly duckling of fairness in machine learning62 cited
  • 2020LiFT61 cited
  • 2023Fairness-aware machine learning engineering: how far are we?60 cited
  • 2022Dbias: detecting biases and ensuring fairness in news articles45 cited
  • 2023“☑ Fairness Toolkits, A Checkbox Culture?” On the Factors that Fragment Developer Practices in Handling Algorithmic Harms39 cited
  • 2023A seven-layer model with checklists for standardising fairness assessment throughout the AI lifecycle31 cited
  • 2025Is artificial intelligence (AI) research biased and conceptually vague? A systematic review of research on bias and discrimination in the context of using AI in human resource management30 cited
  • 2025Towards Fair AI: Mitigating Bias in Credit Decisions—A Systematic Literature Review

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30 cited
  • 2025Software Fairness: An Analysis and Survey23 cited
  • 2020Mitigating Cognitive Biases in Machine Learning Algorithms for Decision Making22 cited
  • 2022Towards model-based bias mitigation in machine learning18 cited
  • 2022Dbias: Detecting biases and ensuring Fairness in news articles13 cited
  • 2022Bias and fairness in machine learning and artificial intelligence11 cited
  • 2024Trustworthy Artificial Intelligence in the Energy Sector: Landscape Analysis and Evaluation Framework11 cited