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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/hanxiao0607/cfad

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Achieving Counterfactual Fairness for Anomaly Detection

https://github.com/hanxudong/fair_enough

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Fair Enough: Standardizing Evaluation and Model Selection for Fairness Research in NLP

https://github.com/hanxudong/fair_nlp_classification

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fairlib: A Unified Framework for Assessing and Improving Classification Fairness

https://github.com/hanxudong/systematic_evaluation_of_predictive_fairness

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Systematic Evaluation of Predictive Fairness

https://github.com/haofanwang/Score-CAM

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Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks

https://github.com/haolun-wu/jmefairness

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Joint Multisided Exposure Fairness for Recommendation

https://github.com/haotiansustc/deepinfo

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Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding

https://github.com/happy2git/guide

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Inductive Graph Unlearning

https://github.com/hardy-education/llm-psychometrics

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"All that Glitters": Approaches to Evaluations with Unreliable Model and Human Annotations

https://github.com/harisalimansoor/fairnessgraphdataimputation

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Impact Of Missing Data Imputation On The Fairness And Accuracy Of Graph Node Classifiers

https://github.com/harshp1802/text-dataset-distillation

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Exploring Multilingual Text Data Distillation

https://github.com/harvard-ophthalmology-ai-lab/fairdiffusion

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FairDiffusion: Enhancing Equity in Latent Diffusion Models via Fair Bayesian Perturbation

https://github.com/harvard-ophthalmology-ai-lab/fairdomain

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FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification

https://github.com/harvard-ophthalmology-ai-lab/fairness_guarantees

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Impact of Data Distribution on Fairness Guarantees in Equitable Deep Learning

https://github.com/harvard-ophthalmology-ai-lab/fairseg

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FairSeg: A Large-Scale Medical Image Segmentation Dataset for Fairness Learning Using Segment Anything Model with Fair Error-Bound Scaling

https://github.com/harvineet/sample-size-fairness-audits

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A Brief Tutorial on Sample Size Calculations for Fairness Audits

https://github.com/hbaniecki/adversarial-explainable-ai

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Adversarial attacks and defenses in explainable artificial intelligence: A survey

https://github.com/hcai-mms/advx-multvae

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Simultaneous Unlearning of Multiple Protected User Attributes From Variational Autoencoder Recommenders Using Adversarial Training

https://github.com/hcorinna/gradual-compatibility

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Gradual (In)Compatibility of Fairness Criteria

https://github.com/hcorinna/utility-based-fairness

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A Justice-Based Framework for the Analysis of Algorithmic Fairness-Utility Trade-Offs

https://github.com/healthylaife/fairalignmentllm

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Aligning (Medical) LLMs for (Counterfactual) Fairness

https://github.com/healthylaife/fairfedavg

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Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods

https://github.com/healthylaife/fairsynth

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Fairness-Optimized Synthetic EHR Generation for Arbitrary Downstream Predictive Tasks

https://github.com/hegehongcha/laymanrrg

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X-ray Made Simple: Lay Radiology Report Generation and Robust Evaluation

https://github.com/hengruilou/deepfacegen

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A Large-scale Universal Evaluation Benchmark For Face Forgery Detection

https://github.com/heqianpei/diga

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Distributionally Generative Augmentation for Fair Facial Attribute Classification

https://github.com/hesfahanizadeh/neuralinformationshaping

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TexShape: Information Theoretic Sentence Embedding for Language Models

https://github.com/heyuntian/fairmile

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FairMILE: Towards an Efficient Framework for Fair Graph Representation Learning

https://github.com/hharcolezi/anonymity-impact-fairness

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Fair Play for Individuals, Foul Play for Groups? Auditing Anonymization's Impact on ML Fairness

https://github.com/hharcolezi/ldp-fairness-impact

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(Local) Differential Privacy has NO Disparate Impact on Fairness

https://github.com/hiaac-finance/credit_pipeline

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Best Practices for Responsible Machine Learning in Credit Scoring

https://github.com/hiaac-finance/m2fgb

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M$^2$FGB: A Min-Max Gradient Boosting Framework for Subgroup Fairness

https://github.com/hil-se/FairBalance

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FairBalance: How to Achieve Equalized Odds With Data Pre-processing

https://github.com/hil-se/RelativeFairnessTesting

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Testing Relative Fairness in Human Decisions With Machine Learning

https://github.com/him229/fultr

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Policy-Gradient Training of Fair and Unbiased Ranking Functions

https://github.com/hjraad/FLT

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Federated Learning with Taskonomy for Non-IID Data

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