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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/aiskunks/ai_research

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Adjusting for Bias with Procedural Data

https://github.com/aivodji/LaundryML

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Fairwashing: the risk of rationalization

https://github.com/aivodji/characterizing_fairwashing

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Characterizing the risk of fairwashing

https://github.com/ajiljalal/code-cs-fairness

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Fairness for Image Generation with Uncertain Sensitive Attributes

https://github.com/akakadiaris/fairnessofmimiciv

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Evaluating the Fairness of the MIMIC-IV Dataset and a Baseline Algorithm: Application to the ICU Length of Stay Prediction

https://github.com/alaaobeid/sedg

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Sum of Group Error Differences: A Critical Examination of Bias Evaluation in Biometric Verification and a Dual-Metric Measure

https://github.com/alan-turing-institute/fairness-monitoring

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Justified Evidence Collection for Argument-based AI Fairness Assurance

https://github.com/alanturin-g/saft

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Size-adaptive Hypothesis Testing for Fairness

https://github.com/alceballosa/clin-robust

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Open (Clinical) LLMs are Sensitive to Instruction Phrasings

https://github.com/alessandro-fabris/ql4facct

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Measuring Fairness Under Unawareness of Sensitive Attributes: A Quantification-Based Approach

https://github.com/alexandersoen/alpha-tree-fair-wrappers

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Fair Wrapping for Black-box Predictions

https://github.com/alexandersoen/fbde

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Fair Densities via Boosting the Sufficient Statistics of Exponential Families

https://github.com/alexmcavoy/fmtl

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Evolutionary instability of selfish learning in repeated games

https://github.com/alfahaf/fair-near-neighbor-search

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Fair Near Neighbor Search: Independent Range Sampling in High Dimensions

https://github.com/alfahaf/fair-nn

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Sampling a Near Neighbor in High Dimensions -- Who is the Fairest of Them All?

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

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A comparative study of fairness-enhancing interventions in machine learning

https://github.com/algowatchpenn/GerryFair

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Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness

https://github.com/alibaba/federatedscope

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pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning

https://github.com/alice1998/recsae

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Interpret the Internal States of Recommendation Model with Sparse Autoencoder

https://github.com/alicedoherty/bayesian-fairness

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Individual Fairness in Bayesian Neural Networks

https://github.com/alivard/ppg

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Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Ranking

https://github.com/allenai/persona-bias

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Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs

https://github.com/alycia-noel/udj-fl

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Achieving Distributive Justice in Federated Learning via Uncertainty Quantification

https://github.com/amazon-research/active-sampling-for-minmax-fairness

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Active Sampling for Min-Max Fairness

https://github.com/amazon-research/fair-ordinal-regression

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Pairwise Fairness for Ordinal Regression

https://github.com/amazon-research/ip-stability-for-clustering

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Individual Preference Stability for Clustering

https://github.com/americast/electoral

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Two-Sided Fairness in Non-Personalised Recommendations

https://github.com/aminchrs/postprocess

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A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problems

https://github.com/amirabbas-afzali/aligning-visual-contrastive-learning-models-via-preference-optimization

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Aligning Visual Contrastive learning models via Preference Optimization

https://github.com/amirarsalan90/TabFairGAN

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TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks

https://github.com/amirhk/recourse

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On the Fairness of Causal Algorithmic Recourse

https://github.com/amishler/fair-double-ensemble-learning

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FADE: FAir Double Ensemble Learning for Observable and Counterfactual Outcomes

https://github.com/amonroym99/iti-gen-reproducibility

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Reproducibility Study of "ITI-GEN: Inclusive Text-to-Image Generation"

https://github.com/amr-keleg/dlama

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DLAMA: A Framework for Curating Culturally Diverse Facts for Probing the Knowledge of Pretrained Language Models

https://github.com/anahideh/FAL--Fair-Active-Learning

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Fair Active Learning

https://github.com/anavaldi/fairness_nsga

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How fair can we go in machine learning? Assessing the boundaries of fairness in decision trees

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