Papers with Code 아카이브

Fairness

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평가표

9

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10

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/abdulazizasz/fairness_sentiment

공식 코드

Balancing Fairness and Accuracy in Sentiment Detection using Multiple Black Box Models

https://github.com/abdullahfsm/pcs

공식 코드

Towards providing reliable job completion time predictions using PCS

https://github.com/abhin02/fair-selective-regression

공식 코드

Selective Regression Under Fairness Criteria

https://github.com/acharaakshit/fairmod

공식 코드

Watching the AI Watchdogs: A Fairness and Robustness Analysis of AI Safety Moderation Classifiers

https://github.com/ad93/FairSumm

공식 코드

Summarizing User-generated Textual Content: Motivation and Methods for Fairness in Algorithmic Summaries

https://github.com/adamkarvonen/llm_bias

공식 코드

Robustly Improving LLM Fairness in Realistic Settings via Interpretability

https://github.com/adamlechowicz/fair-knapsack

공식 코드

Time Fairness in Online Knapsack Problems

https://github.com/adap/flower

공식 코드

FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout

https://github.com/adityavadlamani/conformal-fairness

공식 코드

A Generic Framework for Conformal Fairness

https://github.com/adragnar/irm-toxicity-classification

공식 코드

Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification

https://github.com/adwardlee/t2i_safety

공식 코드

T2ISafety: Benchmark for Assessing Fairness, Toxicity, and Privacy in Image Generation

https://github.com/afm215/FaVGen

공식 코드

Fairer Analysis and Demographically Balanced Face Generation for Fairer Face Verification

https://github.com/agiresearch/up5

공식 코드

UP5: Unbiased Foundation Model for Fairness-aware Recommendation

https://github.com/agronowski/rfib-code

공식 코드

Classification Utility, Fairness, and Compactness via Tunable Information Bottleneck and Rényi Measures

https://github.com/ahachang/defend

공식 코드

Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement

https://github.com/ahmadkhajehnejad/CrossWalk

공식 코드

CrossWalk: Fairness-enhanced Node Representation Learning

https://github.com/ahmed-abouzeid/mmss

공식 코드

Socially Fair Mitigation of Misinformation on Social Networks via Constraint Stochastic Optimization

https://github.com/ahmed-khaled-saleh/fmtl-graph

공식 코드

Learning to Collaborate Over Graphs: A Selective Federated Multi-Task Learning Approach

https://github.com/ahmedcs/refl

공식 코드

Resource-Efficient Federated Learning

https://github.com/ahxt/fair_fairness_benchmark

공식 코드

FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods

https://github.com/ahxt/new_metric_for_demographic_parity

공식 코드

Retiring $Δ$DP: New Distribution-Level Metrics for Demographic Parity

https://github.com/ahxt/yodo

공식 코드

You Only Debias Once: Towards Flexible Accuracy-Fairness Trade-offs at Inference Time

https://github.com/ai-for-decision-making-tue/drl-guided-picker-optimization

공식 코드

Learning Efficient and Fair Policies for Uncertainty-Aware Collaborative Human-Robot Order Picking

https://github.com/ai-secure/certified-fairness

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Certifying Some Distributional Fairness with Subpopulation Decomposition

https://github.com/ai4life-group/fair-unlearning

공식 코드

Fair Machine Unlearning: Data Removal while Mitigating Disparities

https://github.com/ai4life-group/openxai

공식 코드

OpenXAI: Towards a Transparent Evaluation of Model Explanations

https://github.com/aiandglobaldevelopmentlab/textdisentanglement

공식 코드

Can Large Language Models (or Humans) Disentangle Text?

https://github.com/aida-ugent/DeBayes

공식 코드

DeBayes: a Bayesian Method for Debiasing Network Embeddings

https://github.com/aida-ugent/FIPR

공식 코드

The KL-Divergence between a Graph Model and its Fair I-Projection as a Fairness Regularizer

https://github.com/aida-ugent/OTF

공식 코드

Optimal Transport of Classifiers to Fairness

https://github.com/aida-ugent/abcfair

공식 코드

ABCFair: an Adaptable Benchmark approach for Comparing Fairness Methods

https://github.com/aida-ugent/fairret

공식 코드

fairret: a Framework for Differentiable Fairness Regularization Terms

https://github.com/aida-ugent/feir

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FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources

https://github.com/aiflames/flames

공식 코드

Flames: Benchmarking Value Alignment of LLMs in Chinese

https://github.com/ailiaili/difference_mean_fair_models

공식 코드

Optimising Equal Opportunity Fairness in Model Training

https://github.com/aimat-lab/megan_global_explanations

공식 코드

Global Concept Explanations for Graphs by Contrastive Learning

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