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Fairness

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코드

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

https://github.com/diazf/pref_eval

공식 코드

Recall, Robustness, and Lexicographic Evaluation

https://github.com/dimichai/mo-tndp

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Scalable Multi-Objective Reinforcement Learning with Fairness Guarantees using Lorenz Dominance

https://github.com/discovershu/dro_cox

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Distributionally Robust Survival Analysis: A Novel Fairness Loss Without Demographics

https://github.com/discovershu/dro_survival

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Fairness in Survival Analysis with Distributionally Robust Optimization

https://github.com/dkollias/fair-consistent-affect-analysis

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Bridging the Gap: Protocol Towards Fair and Consistent Affect Analysis

https://github.com/dmah10/fairness-explainable-nlp

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Gender Bias in Explainability: Investigating Performance Disparity in Post-hoc Methods

https://github.com/domkowald/fairrecsys

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Investigating Popularity Bias Amplification in Recommender Systems Employed in the Entertainment Domain

https://github.com/donglgcn/fairac

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Fair Attribute Completion on Graph with Missing Attributes

https://github.com/dooleys/fr-nas

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Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition

https://github.com/dooleys/robustness

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Robustness Disparities in Face Detection

https://github.com/dooleys/robustness-disparities-in-commercial-face-detection

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Robustness Disparities in Face Detection

https://github.com/dorisscma/fairgridsearch

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FairGridSearch: A Framework to Compare Fairness-Enhancing Models

https://github.com/dplecko/causal-acc-decomp

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Fairness-Accuracy Trade-Offs: A Causal Perspective

https://github.com/dracosource/draco

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Multi-Agent Distributed Reinforcement Learning for Making Decentralized Offloading Decisions

https://github.com/dsachar/auditspatialfairness

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Auditing for Spatial Fairness

https://github.com/dscarvalho/modifiers_consistency

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Montague semantics and modifier consistency measurement in neural language models

https://github.com/dsolanno/Poisoning-Attacks-on-Algorithmic-Fairness

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Poisoning Attacks on Algorithmic Fairness

https://github.com/dsrestrepo/lab-mae-foundation-tabular

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Representation Learning of Lab Values via Masked AutoEncoder

https://github.com/dssg/aequitas

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Aequitas: A Bias and Fairness Audit Toolkit

https://github.com/dssg/hiv-retention-public

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A Machine Learning System for Retaining Patients in HIV Care

https://github.com/dssg/peeps-chili

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Empirical observation of negligible fairness-accuracy trade-offs in machine learning for public policy

https://github.com/dtak/ocbnn-public

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Incorporating Interpretable Output Constraints in Bayesian Neural Networks

https://github.com/duncanmcelfresh/RobustKidneyExchange

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Scalable Robust Kidney Exchange

https://github.com/dutchhansen/empirical-multicalibration

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When is Multicalibration Post-Processing Necessary?

https://github.com/dutchhansen/multicalibration

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When is Multicalibration Post-Processing Necessary?

https://github.com/dvlab-research/parametric-contrastive-learning

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Classes Are Not Equal: An Empirical Study on Image Recognition Fairness

https://github.com/dylan-slack/fairness-warnings-fair-maml

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Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data

https://github.com/eaglelab-zju/opengt

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OpenGT: A Comprehensive Benchmark For Graph Transformers

https://github.com/easeml/datascope

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Data Debugging with Shapley Importance over End-to-End Machine Learning Pipelines

https://github.com/ec-jrc/humaint_annotator

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Attribute Annotation and Bias Evaluation in Visual Datasets for Autonomous Driving

https://github.com/ece7048/3dsulci

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An explainable three dimension framework to uncover learning patterns: A unified look in variable sulci recognition

https://github.com/ecreager/causal-dyna-fair

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Causal Modeling for Fairness in Dynamical Systems

https://github.com/ecreager/eiil

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Environment Inference for Invariant Learning

https://github.com/edapinenut/cbgbench

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CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph

https://github.com/edavalosanaya/LLMsAsEducationalAnalysts

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LLMs as Educational Analysts: Transforming Multimodal Data Traces into Actionable Reading Assessment Reports

https://github.com/eduardacaldeira/compression_bias_survey

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Model Compression Techniques in Biometrics Applications: A Survey

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