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Fairness

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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/igraugar/fcp

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Forward Composition Propagation for Explainable Neural Reasoning

https://github.com/iiscleap/Coswara-Data

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Coswara: A respiratory sounds and symptoms dataset for remote screening of SARS-CoV-2 infection

https://github.com/ilyaamburg/fair-clustering-for-diverse-and-experienced-groups

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Hypergraph Clustering for Finding Diverse and Experienced Groups

https://github.com/imrecommender/chatgpt-news

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A Preliminary Study of ChatGPT on News Recommendation: Personalization, Provider Fairness, Fake News

https://github.com/imtiazziko/Clustering-with-fairness-constraints

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Variational Fair Clustering

https://github.com/infinite-pursuits/fairproof

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FairProof : Confidential and Certifiable Fairness for Neural Networks

https://github.com/infinite-pursuits/influence-based-attributions-can-be-manipulated

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Influence-based Attributions can be Manipulated

https://github.com/informsjoc/2024.0651

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Adaptive Bounded Exploration and Intermediate Actions for Data Debiasing

https://github.com/inouye-lab/ild-domain-counterfactuals

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Towards Characterizing Domain Counterfactuals For Invertible Latent Causal Models

https://github.com/inouye-lab/pcf

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Counterfactual Fairness by Combining Factual and Counterfactual Predictions

https://github.com/integrated-intelligence-lab/canonical_sets

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LUCID-GAN: Conditional Generative Models to Locate Unfairness

https://github.com/iodmitri/gradelab

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Grade Score: Quantifying LLM Performance in Option Selection

https://github.com/iosifidisvasileios/AdaFair

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AdaFair: Cumulative Fairness Adaptive Boosting

https://github.com/ipieter/biased-rulers

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Measuring Fairness with Biased Rulers: A Survey on Quantifying Biases in Pretrained Language Models

https://github.com/irfanmee/eab-fl

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EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learning

https://github.com/irisdominguez/dataset-bias-metrics

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Assessing Demographic Bias Transfer from Dataset to Model: A Case Study in Facial Expression Recognition

https://github.com/irisdominguez/dataset_bias_metrics

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Metrics for Dataset Demographic Bias: A Case Study on Facial Expression Recognition

https://github.com/isarnejad/procedural-fairness-sentiment

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Towards Procedural Fairness: Uncovering Biases in How a Toxic Language Classifier Uses Sentiment Information

https://github.com/istaustria-cvml/flea

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FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data

https://github.com/itm-kitware/llm-alignable-dm

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Language Models are Alignable Decision-Makers: Dataset and Application to the Medical Triage Domain

https://github.com/itsmag11/aitti

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AITTI: Learning Adaptive Inclusive Token for Text-to-Image Generation

https://github.com/iurada/fairness_crossdomain

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Fairness meets Cross-Domain Learning: a new perspective on Models and Metrics

https://github.com/ivonacolakovic/synthgap-mitigation-using-kd-in-ffr

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How Knowledge Distillation Mitigates the Synthetic Gap in Fair Face Recognition

https://github.com/j-yuan/gequality

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Group Equality in Adaptive Submodular Maximization

https://github.com/j2kun/fkl-SDM16

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A Confidence-Based Approach for Balancing Fairness and Accuracy

https://github.com/jackblandin/fairness-comparison

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Fairness Through Counterfactual Utilities

https://github.com/jackhck/madaug

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When to Learn What: Model-Adaptive Data Augmentation Curriculum

https://github.com/jackmedda/c-fairness-recsys

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Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations

https://github.com/jackmedda/cpfairrobust

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Robustness in Fairness against Edge-level Perturbations in GNN-based Recommendation

https://github.com/jackmedda/fa4gcf

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Fair Augmentation for Graph Collaborative Filtering

https://github.com/jackmedda/rs-bgexplainer

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GNNUERS: Fairness Explanation in GNNs for Recommendation via Counterfactual Reasoning

https://github.com/jacob-roth/superquantile-opt

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Fast Computation of Superquantile-Constrained Optimization Through Implicit Scenario Reduction

https://github.com/jacyanthis/causal-context

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Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness

https://github.com/jakefawkes/fragile_fair

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The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning

https://github.com/jamesluoyh/frg

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Learning Fair Representations with High-Confidence Guarantees

https://github.com/jaripeeperkorn/Group-Fairness-in-Predictive-Process-Monitoring

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Achieving Group Fairness through Independence in Predictive Process Monitoring

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