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Fairness

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/HarrieO/2021-SIGIR-plackett-luce

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Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness

https://github.com/Harvard-Ophthalmology-AI-Lab/FairCLIP

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FairCLIP: Harnessing Fairness in Vision-Language Learning

https://github.com/HoinJung/Unified-Debiaisng-VLM-SFID

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A Unified Debiasing Approach for Vision-Language Models across Modalities and Tasks

https://github.com/HongshengHu/membership-inference-machine-learning-literature

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Membership Inference Attacks on Machine Learning: A Survey

https://github.com/HowieHwong/TrustLLM

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TrustLLM: Trustworthiness in Large Language Models

https://github.com/HristoInouzhe/AttractionRepulsionClustering

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Attraction-Repulsion clustering with applications to fairness

https://github.com/HuiHu1/Distributed-Private-Fair-Learning

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A Distributed Fair Machine Learning Framework with Private Demographic Data Protection

https://github.com/HyberionBrew/difflogic_verify

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Logic Gate Neural Networks are Good for Verification

https://github.com/IBM/AIF360

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AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias

https://github.com/IBM/UQ360

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Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI

https://github.com/IBM/devil-in-GAN

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The Devil is in the GAN: Backdoor Attacks and Defenses in Deep Generative Models

https://github.com/IBM/sensitive-subspace-robustness

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Training individually fair ML models with Sensitive Subspace Robustness

https://github.com/INK-USC/expl-refinement

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Refining Language Models with Compositional Explanations

https://github.com/ITZuern/On-Baselines-for-Local-Feature-Attributions

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On Baselines for Local Feature Attributions

https://github.com/JPurdy-ORRAI/ORRAI_AlgFairnessCorrectionDemo

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The Pursuit of Algorithmic Fairness: On "Correcting" Algorithmic Unfairness in a Child Welfare Reunification Success Classifier

https://github.com/JRConti/EthicalModule_vMF

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Mitigating Gender Bias in Face Recognition Using the von Mises-Fisher Mixture Model

https://github.com/Jaroan/Fair-MARL

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Cooperation and Fairness in Multi-Agent Reinforcement Learning

https://github.com/Jiaxin-Xu/PairwiseShapley

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From Abstract to Actionable: Pairwise Shapley Values for Explainable AI

https://github.com/Jittor/JDet

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Theoretically Achieving Continuous Representation of Oriented Bounding Boxes

https://github.com/JoaoVitorPamplona/Fair-Generalized-Linear-Mixed-Models

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Fair Generalized Linear Mixed Models

https://github.com/JunhuiShen/Fair-PCA-via-EigOpt

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Hidden Convexity of Fair PCA and Fast Solver via Eigenvalue Optimization

https://github.com/Kexin-Fan/Multi-Source-Analysing

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Urinary Tract Infection Detection in Digital Remote Monitoring: Strategies for Managing Participant-Specific Prediction Complexity

https://github.com/KeyWgh/FedGFT

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Mitigating Group Bias in Federated Learning: Beyond Local Fairness

https://github.com/KhalilDMK/EBPR

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Debiased Explainable Pairwise Ranking from Implicit Feedback

https://github.com/LalehSeyyed/CheXclusion

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CheXclusion: Fairness gaps in deep chest X-ray classifiers

https://github.com/LisaKouts/Implicit_bias_FCMs

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Modeling Implicit Bias with Fuzzy Cognitive Maps

https://github.com/LisaKouts/Implicit_bias_FCMs/tree/main/Code_IntelliSys2023_submission

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Measuring Implicit Bias Using SHAP Feature Importance and Fuzzy Cognitive Maps

https://github.com/LorenzoCazzaro/explainable-global-fairness-verification

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Explainable Global Fairness Verification of Tree-Based Classifiers

https://github.com/LuoZhhh/NIFA

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Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injections

https://github.com/MLforHealth/HurtfulWords

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Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings

https://github.com/MLforHealth/predictive_checklists

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Learning Optimal Predictive Checklists

https://github.com/MSoumm/FaVFA

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Fairer Analysis and Demographically Balanced Face Generation for Fairer Face Verification

https://github.com/Ma-Lab-Public/UX-SPR

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Recommending POIs for Tourists by User Behavior Modeling and Pseudo-Rating

https://github.com/MarcoMorik/Dynamic-Fairness

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Controlling Fairness and Bias in Dynamic Learning-to-Rank

https://github.com/MattJonesMath/Irrelevant_Voters_Project

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New fairness criteria for truncated ballots in multi-winner ranked-choice elections

https://github.com/MengtingWan/marketBias

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Addressing Marketing Bias in Product Recommendations

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