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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/pimpraat/recsysproject

공식 코드

[RE] Modeling Personalized Item Frequency Information for Next-basket Recommendation

https://github.com/pku-ml/cfa

공식 코드

CFA: Class-wise Calibrated Fair Adversarial Training

https://github.com/pl97/constr_fl

공식 코드

Federated Learning with Convex Global and Local Constraints

https://github.com/pliang279/LM_bias

공식 코드

Towards Understanding and Mitigating Social Biases in Language Models

https://github.com/pluslabnlp/clipscore-bias

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Gender Biases in Automatic Evaluation Metrics for Image Captioning

https://github.com/pmangold/fairness-privacy

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Differential Privacy has Bounded Impact on Fairness in Classification

https://github.com/pnokhiz/precarity

공식 코드

Precarity: Modeling the Long Term Effects of Compounded Decisions on Individual Instability

https://github.com/poloclub/FairVis

공식 코드

FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning

https://github.com/praal/policy-aggregation

공식 코드

Policy Aggregation

https://github.com/praal/remembering-to-be-fair

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Remembering to Be Fair: Non-Markovian Fairness in Sequential Decision Making

https://github.com/pranabiitp/fedmrl

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FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical Imaging

https://github.com/prateeky2806/emc-cols-recourse

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Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions

https://github.com/praveenv253/ann-info-flow

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Can Information Flows Suggest Targets for Interventions in Neural Circuits?

https://github.com/predictive-analytics-lab/Data-Domain-Fairness

공식 코드

Discovering Fair Representations in the Data Domain

https://github.com/predictive-analytics-lab/UniversalGP

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Tuning Fairness by Balancing Target Labels

https://github.com/predictive-analytics-lab/nifr

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Null-sampling for Interpretable and Fair Representations

https://github.com/preethisesh/hiring_fairness

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Who Does the Giant Number Pile Like Best: Analyzing Fairness in Hiring Contexts

https://github.com/princeton-nlp/mabel

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MABEL: Attenuating Gender Bias using Textual Entailment Data

https://github.com/princetonvisualai/DomainBiasMitigation

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Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation

https://github.com/princetonvisualai/directional-bias-amp

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Directional Bias Amplification

https://github.com/princetonvisualai/gan-debiasing

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Fair Attribute Classification through Latent Space De-biasing

https://github.com/princetonvisualai/intersectionality

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Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation

https://github.com/privacytrustlab/bias_in_FL

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Bias Propagation in Federated Learning

https://github.com/probcomp/sppl

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SPPL: Probabilistic Programming with Fast Exact Symbolic Inference

https://github.com/psanch21/VACA

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VACA: Design of Variational Graph Autoencoders for Interventional and Counterfactual Queries

https://github.com/psunlpgroup/fairsumm

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Fair Abstractive Summarization of Diverse Perspectives

https://github.com/pterhoer/DeepFakeAnnotations

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Analyzing Fairness in Deepfake Detection With Massively Annotated Databases

https://github.com/pterhoer/FairScoreNormalization

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Post-Comparison Mitigation of Demographic Bias in Face Recognition Using Fair Score Normalization

https://github.com/pth1993/fatdm

공식 코드

Fairness and Accuracy under Domain Generalization

https://github.com/purdue-m2/ai-face-fairnessbench

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AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmark

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

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Preserving AUC Fairness in Learning with Noisy Protected Groups

https://github.com/purdue-m2/fairness-generalization

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Preserving Fairness Generalization in Deepfake Detection

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

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Robust Fairness Vision-Language Learning for Medical Image Analysis

https://github.com/pysentimiento/pysentimiento

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pysentimiento: A Python Toolkit for Opinion Mining and Social NLP tasks

https://github.com/qiangyao1988/dsa

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Fairness-aware Vision Transformer via Debiased Self-Attention

https://github.com/qimingwu/fedida

공식 코드

Toward Fair Federated Learning under Demographic Disparities and Data Imbalance

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