SZodixStudyAcademic Library
ShelvesOpen access
Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated
Interpreting Unfairness in Graph Neural Networks via Training Node Attribution
16 references·0 citing·31 second hop
Interpreting Unfairness in Graph Neural Networks via Training Node Attribution (2023)Interpreting Unfairness in Graph Neural N…Fairness in Machine Learning: A Survey (2023)Fairness in Machine Learning:…Fairness in Graph Mining: A Survey (2023)Fairness in Graph Mining: A S…Algorithmic Fairness (2023)Algorithmic FairnessLearning Fair Graph Neural Networks with Limited and Private Sensitive Attribute Information (2022)Learning Fair Graph Neural Ne…Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage (2022)Improving Fairness in Graph N…FAIR: Fair adversarial instance re-weighting (2022)FAIR: Fair adversarial instan…EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks (2022)EDITS: Modeling and Mitigatin…Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information (2021)Say No to the Discrimination:…On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections (2021)On Dyadic Fairness: Exploring…A Survey on Bias and Fairness in Machine Learning (2021)A Survey on Bias and Fairness…Fairness in Deep Learning: A Computational Perspective (2020)Fairness in Deep Learning: A …Algorithmic Fairness: Choices, Assumptions, and Definitions (2020)Algorithmic Fairness: Choices…iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making (2019)iFair: Learning Individually …Identifying and Correcting Label Bias in Machine Learning (2019)Identifying and Correcting La…Equality of Opportunity in Supervised Learning (2016)Equality of Opportunity in Su…Fairness through awareness (2012)Fairness through awarenessIncorporating Fairness into Game Theory and Economics (2011)How the machine ‘thinks’: Understanding opacity in machine learning algorithms (2016)Princeton University Press (2017)Big Data's Disparate Impact (2014)ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope (2023)Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments (2017)DGHNL: A new deep genetic hierarchical network of learners for prediction of credit scoring (2019)Fairness without Demographics through Adversarially Reweighted Learning (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)Certifying and Removing Disparate Impact (2015)Artificial Intelligence Governance For Businesses (2022)AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications (2023)CRITICAL QUESTIONS FOR BIG DATA (2012)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)A theory of justice original editionHealth Care Disparities (2003)Towards a standard for identifying and managing bias in artificial intelligence (2022)Bias and Unfairness in Machine Learning Models: A Systematic Review on Datasets, Tools, Fairness Metrics, and Identification and Mitigation Methods (2023)A Theory of Justice (1971)The Filter Bubble: What the Internet Is Hiding from You (2011)Representation Bias in Data: A Survey on Identification and Resolution Techniques (2023)Fairness and Bias in Algorithmic Hiring: A Multidisciplinary Survey (2024)Policy advice and best practices on bias and fairness in AI (2024)Towards Debiasing Fact Verification Models (2019)A survey on bias in visual datasets (2022)
this work references cited by second hop

References

  • 2021A Survey on Bias and Fairness in Machine Learning5,798 cited
  • 2012Fairness through awareness3,501 cited
  • 2016Equality of Opportunity in Supervised Learning2,535 cited
  • 2020Algorithmic Fairness: Choices, Assumptions, and Definitions687 cited
  • 2023Fairness in Machine Learning: A Survey645 cited
  • 2021Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information247 cited
  • 2020Fairness in Deep Learning: A Computational Perspective216 cited
  • 2019iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making162 cited
  • 2019Identifying and Correcting Label Bias in Machine Learning152 cited
  • 2023Fairness in Graph Mining: A Survey138 cited
  • 2022EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks125 cited
  • 2023Algorithmic Fairness104 cited
  • 2022Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage95 cited
  • 2021On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections45 cited
  • 2022FAIR: Fair adversarial instance re-weighting40 cited
  • 2022

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

Learning Fair Graph Neural Networks with Limited and Private Sensitive Attribute Information
39 cited

Cited by

No citing works inside the corpus for this window.