SZodixStudyAcademic Library
ShelvesOpen access
Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated
Estimating and Improving Fairness with Adversarial Learning
10 references·1 citing·39 second hop
Estimating and Improving Fairness with Adversarial Learning (2021)Estimating and Improving Fairness with Ad…Risk of Training Diagnostic Algorithms on Data with Demographic Bias (2020)Risk of Training Diagnostic A…Intra-Processing Methods for Debiasing Neural Networks (2020)Intra-Processing Methods for …Fairness in Deep Learning: A Computational Perspective (2020)Fairness in Deep Learning: A …Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations (2019)Balanced Datasets Are Not Eno…Women Also Snowboard: Overcoming Bias in Captioning Models (2018)Women Also Snowboard: Overcom…Mitigating Unwanted Biases with Adversarial Learning (2018)Mitigating Unwanted Biases wi…FairGAN: Fairness-aware Generative Adversarial Networks (2018)FairGAN: Fairness-aware Gener…AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias (2018)AI Fairness 360: An Extensibl…Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations (2017)Data Decisions and Theoretica…Fairness through awareness (2012)Fairness through awarenessFair Machine Learning in Healthcare: A Survey (2024)Fair Machine Learning in Heal…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)A Survey on Bias and Fairness in Machine Learning (2021)Learning Fair Representations (2013)Equality of Opportunity in Supervised Learning (2016)Measuring and Mitigating Unintended Bias in Text Classification (2018)Fairness in Machine Learning: A Survey (2023)Inherent Tradeoffs in the Fair Determination of Risk Scores (2023)Inherent Trade-Offs in the Fair Determination of Risk Scores (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)Certifying and Removing Disparate Impact (2015)Aequitas: A Bias and Fairness Audit Toolkit (2018)Artificial Discretion as a Tool of Governance: A Framework for Understanding the Impact of Artificial Intelligence on Public Administration (2019)FactSheets: Increasing trust in AI services through supplier's declarations of conformity (2019)Big Data's Disparate Impact (2014)Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020)Data and its (dis)contents: A survey of dataset development and use in machine learning research (2021)A Review on Fairness in Machine Learning (2022)Semantics derived automatically from language corpora contain human-like biases (2017)Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification (2018)Demographic Bias in Biometrics: A Survey on an Emerging Challenge (2020)Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation (2020)On the Opportunities and Risks of Foundation Models (2021)AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias (2019)Model Cards for Model Reporting (2019)FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis (2022)Algorithmic fairness in precision psychiatry: analysis of prediction models in individuals at clinical high risk for psychosis (2023)Ethical considerations in AI-based user profiling for knowledge management: A critical review (2025)Bias in data‐driven artificial intelligence systems—An introductory survey (2020)Algorithmic fairness in artificial intelligence for medicine and healthcare (2023)Artificial Intelligence Governance For Businesses (2022)AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications (2023)Bias and Unfairness in Machine Learning Models: A Systematic Review on Datasets, Tools, Fairness Metrics, and Identification and Mitigation Methods (2023)Bias and Fairness in Large Language Models: A Survey (2024)Gender Bias in Natural Language Processing and Computer Vision: A Comparative Survey (2024)Improving Fairness using Vision-Language Driven Image Augmentation (2024)
this work references cited by second hop

References

  • 2012Fairness through awareness3,545 cited
  • 2018Mitigating Unwanted Biases with Adversarial Learning1,428 cited
  • 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias437 cited
  • 2018Women Also Snowboard: Overcoming Bias in Captioning Models404 cited
  • 2018FairGAN: Fairness-aware Generative Adversarial Networks381 cited
  • 2019Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations359 cited
  • 2017Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations353 cited
  • 2020Fairness in Deep Learning: A Computational Perspective216 cited
  • 2020Risk of Training Diagnostic Algorithms on Data with Demographic Bias54 cited
  • 2020Intra-Processing Methods for Debiasing Neural Networks22 cited

Cited by

  • 2024Fair Machine Learning in Healthcare: A Survey12 cited

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