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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/patrikken/fair-dsr/blob/main/src/FAIRDA/fair_da.py

Fair Classification via Domain Adaptation: A Dual Adversarial Learning Approach

https://github.com/pku-yuangroup/open-sora-plan

Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution

https://github.com/pps-lab/fl-analysis

Attack of the Tails: Yes, You Really Can Backdoor Federated Learning

https://github.com/privacytrustlab/adversarial_bias

On Adversarial Bias and the Robustness of Fair Machine Learning

https://github.com/pterhoer/FaceImageQuality

Face Quality Estimation and Its Correlation to Demographic and Non-Demographic Bias in Face Recognition

https://github.com/purdue-m2/covid-19_detection_m2_purdue

Domain adaptation, Explainability & Fairness in AI for Medical Image Analysis: Diagnosis of COVID-19 based on 3-D Chest CT-scans

https://github.com/purdue-m2/robust_fairness_withNoisy_labels

Analyzing Fairness in Deepfake Detection With Massively Annotated Databases

https://github.com/rashid-islam/Differential_Fairness

An Intersectional Definition of Fairness

https://github.com/rastegarpanah/antidote-data-framework

Fighting Fire with Fire: Using Antidote Data to Improve Polarization and Fairness of Recommender Systems

https://github.com/realdingke/mscai_softwareengineering_project

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

https://github.com/reclist/evalrs-cikm-2022

Track2Vec: fairness music recommendation with a GPU-free customizable-driven framework

https://github.com/revbucket/lipMIP

Exactly Computing the Local Lipschitz Constant of ReLU Networks

https://github.com/rfordatascience/tidytuesday

GPT detectors are biased against non-native English writers

https://github.com/rhythmswing/Fair-Representation-Learning

Learning Fair Representations via an Adversarial Framework

https://github.com/rigorous-software-engineering/nomos

Specifying and Testing $k$-Safety Properties for Machine-Learning Models

https://github.com/rmit-ir/fair-arguments

Evaluating Fairness in Argument Retrieval

https://github.com/rohinmanvi/GeoLLM

Large Language Models are Geographically Biased

https://github.com/rsCPSyEu/ovd_cod

ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models

https://github.com/rvr-account/rvr

Learning Adversarially Fair and Transferable Representations

https://github.com/s-huu/pmtl

Ditto: Fair and Robust Federated Learning Through Personalization

https://github.com/s-lora/s-lora

Fairness in Serving Large Language Models

https://github.com/saharaja/fairness

Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics

https://github.com/saist1993/fairgrad

FairGrad: Fairness Aware Gradient Descent

https://github.com/samuel-yeom/fliptest

FlipTest: Fairness Testing via Optimal Transport

https://github.com/sanchom/algorithmic-decision-making-and-rule-of-law

Censoring Representations with an Adversary

https://github.com/savvinadaniil/biasinrecommendation

Hidden Author Bias in Book Recommendation

https://github.com/scikit-learn-contrib/categorical-encoding

Fairness Implications of Encoding Protected Categorical Attributes

https://github.com/scikit-learn-contrib/category_encoders

Fairness Implications of Encoding Protected Categorical Attributes

https://github.com/seominseok0429/vidoe_tracking-Reidentification-Action_recognition_Demo

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

https://github.com/sethneel/GerryFair

Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness

https://github.com/shangoma/Hate_Speech

Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition

https://github.com/shizuo-kaji/PretrainCNNwithNoData

A Survey on Bias and Fairness in Machine Learning

https://github.com/shreejalt/freematch-pytorch

FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning

https://github.com/sincerass/mvlpt

ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models

https://github.com/siyi-wind/STiL

FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning

https://github.com/skyve2012/DBA

Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification

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