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Fairness

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하위 태스크

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평가표

9

보존된 평가 기록

10

코드

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

https://github.com/evdoxiataka/effi_user_study1

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Human-in-the-loop Fairness: Integrating Stakeholder Feedback to Incorporate Fairness Perspectives in Responsible AI

https://github.com/evdoxiataka/effi_user_study2

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Human-in-the-loop Fairness: Integrating Stakeholder Feedback to Incorporate Fairness Perspectives in Responsible AI

https://github.com/evgchz/dpabst

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Classification with abstention but without disparities

https://github.com/evijit/SIGIR_FairRanking_UncertainInference

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When Fair Ranking Meets Uncertain Inference

https://github.com/evijit/awareness_vs_unawareness

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When Fair Classification Meets Noisy Protected Attributes

https://github.com/ewoet/intrinsic-gender-probe

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How Far Can It Go?: On Intrinsic Gender Bias Mitigation for Text Classification

https://github.com/ewsheng/decoding-biases

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Societal Biases in Language Generation: Progress and Challenges

https://github.com/f-u-njoku/many-objective-fs-nsgaiii

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A data-science pipeline to enable the Interpretability of Many-Objective Feature Selection

https://github.com/faaizt/datasetfairness

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Achievable Fairness on Your Data With Utility Guarantees

https://github.com/fabien-couthouis/xai-in-rl

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Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley Values

https://github.com/facebookresearch/responsiblenlp

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Perturbation Augmentation for Fairer NLP

https://github.com/facebookresearch/vissl

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Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision

https://github.com/faezahmed/diverse_matching

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Diverse Weighted Bipartite b-Matching

https://github.com/fahim-sikder/fairx

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FairX: A comprehensive benchmarking tool for model analysis using fairness, utility, and explainability

https://github.com/fairdeface/FairDeface

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FairDeFace: Evaluating the Fairness and Adversarial Robustness of Face Obfuscation Methods

https://github.com/fairinterpret/fair_images

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Fairness Explainability using Optimal Transport with Applications in Image Classification

https://github.com/fairml-research/Counterfactual_Fairness

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Adversarial Learning for Counterfactual Fairness

https://github.com/fairml-research/HGR_NN

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Fairness-Aware Neural Réyni Minimization for Continuous Features

https://github.com/fairml-research/SRCVAE

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Fairness without the sensitive attribute via Causal Variational Autoencoder

https://github.com/fairmlresearch/road

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On the Fairness ROAD: Robust Optimization for Adversarial Debiasing

https://github.com/fairnas/FairNAS

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FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search

https://github.com/fairnessresearch/fairness-testing-of-autonomous-driving-systems

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Bias Behind the Wheel: Fairness Testing of Autonomous Driving Systems

https://github.com/faisalhamman/fairfl-pid

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Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition

https://github.com/falaaharifkhan/data-cleaning-stability

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Still More Shades of Null: An Evaluation Suite for Responsible Missing Value Imputation

https://github.com/familyld/insightfair

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What Hides behind Unfairness? Exploring Dynamics Fairness in Reinforcement Learning

https://github.com/fangru-lin/redial_dialect_robustness_fairness

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One Language, Many Gaps: Evaluating Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks

https://github.com/fanny-jourdan/taco

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TaCo: Targeted Concept Erasure Prevents Non-Linear Classifiers From Detecting Protected Attributes

https://github.com/fat-forensics/fat-forensics

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FAT Forensics: A Python Toolbox for Algorithmic Fairness, Accountability and Transparency

https://github.com/fat-forensics/fatf-dashboard

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FAT Forensics: A Python Toolbox for Algorithmic Fairness, Accountability and Transparency

https://github.com/fat-forensics/resources

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FAT Forensics: A Python Toolbox for Algorithmic Fairness, Accountability and Transparency

https://github.com/fbiying87/demographic-bias-visualization

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Towards Explaining Demographic Bias through the Eyes of Face Recognition Models

https://github.com/fedcome/fedcome

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Federated Learning via Consensus Mechanism on Heterogeneous Data: A New Perspective on Convergence

https://github.com/feedzai/bank-account-fraud

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Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation

https://github.com/feedzai/fair-automl

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Promoting Fairness through Hyperparameter Optimization

https://github.com/feedzai/fair-obnc

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Fair-OBNC: Correcting Label Noise for Fairer Datasets

https://github.com/feedzai/fairgbm

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FairGBM: Gradient Boosting with Fairness Constraints

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.