Papers with Code 아카이브

Fairness

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평가표

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보존된 평가 기록

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코드

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

https://github.com/comp-well-org/fair-tab-diffusion

공식 코드

Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models

https://github.com/compl-ai/compl-ai

공식 코드

COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Act

https://github.com/computer-vision-in-the-wild/cvinw_readings

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ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models

https://github.com/conradborchers/abroca-sim

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ABROCA Distributions For Algorithmic Bias Assessment: Considerations Around Interpretation

https://github.com/cpjku/modularizeddebiasing

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Parameter-efficient Modularised Bias Mitigation via AdapterFusion

https://github.com/crherlihy/prob_fair_rmab

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Planning to Fairly Allocate: Probabilistic Fairness in the Restless Bandit Setting

https://github.com/criteo-research/fairjob-dataset

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FairJob: A Real-World Dataset for Fairness in Online Systems

https://github.com/cruiseresearchgroup/CAPRI-FAIR

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CAPRI-FAIR: Integration of Multi-sided Fairness in Contextual POI Recommendation Framework

https://github.com/csebuetnlp/BanglaEmotionBias

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An Empirical Study of Gendered Stereotypes in Emotional Attributes for Bangla in Multilingual Large Language Models

https://github.com/cshaitao/lexeval

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LexEval: A Comprehensive Chinese Legal Benchmark for Evaluating Large Language Models

https://github.com/cshvienna/hyper_fair

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hyperFA*IR: A hypergeometric approach to fair rankings with finite candidate pool

https://github.com/csml-iit-ucl/gmfbandits

공식 코드

Group Meritocratic Fairness in Linear Contextual Bandits

https://github.com/cssh-rwth/fairceptron

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The FairCeptron: A Framework for Measuring Human Perceptions of Algorithmic Fairness

https://github.com/cuis15/FCFL

공식 코드

Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning

https://github.com/cuis15/xorder

공식 코드

Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility

https://github.com/cuishiyao96/fft

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FFT: Towards Harmlessness Evaluation and Analysis for LLMs with Factuality, Fairness, Toxicity

https://github.com/cure-lab/evaluateaigc

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Evaluating Text-to-Image Generative Models: An Empirical Study on Human Image Synthesis

https://github.com/cvs-health/langfair

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An Actionable Framework for Assessing Bias and Fairness in Large Language Model Use Cases

https://github.com/cwei01/fairdgcl

공식 코드

FairDgcl: Fairness-aware Recommendation with Dynamic Graph Contrastive Learning

https://github.com/cylumn/qd-generative-sampling

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Quality-Diversity Generative Sampling for Learning with Synthetic Data

https://github.com/cyz-ai/infomin

공식 코드

Scalable Infomin Learning

https://github.com/daminz97/multi-step_gsv

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Hire Me or Not? Examining Language Model's Behavior with Occupation Attributes

https://github.com/danielkty/debiasing-rag

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Mitigating Bias in RAG: Controlling the Embedder

https://github.com/danishpruthi/deceptive-attention

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Learning to Deceive with Attention-Based Explanations

https://github.com/danwley/globe-ce

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GLOBE-CE: A Translation-Based Approach for Global Counterfactual Explanations

https://github.com/david-jenny/llm-political-study

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Exploring the Jungle of Bias: Political Bias Attribution in Language Models via Dependency Analysis

https://github.com/david-li0406/smoa

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SMoA: Improving Multi-agent Large Language Models with Sparse Mixture-of-Agents

https://github.com/davidberend/FairnessMatters

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Fair and accurate age prediction using distribution aware data curation and augmentation

https://github.com/dazhuoq/counterfactual_evidence

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Finding Counterfactual Evidences for Node Classification

https://github.com/dd1github/fair-over-sampling

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Towards A Holistic View of Bias in Machine Learning: Bridging Algorithmic Fairness and Imbalanced Learning

https://github.com/ddsb01/Fair-kFood

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Towards Fairness in Online Service with k Servers and its Application on Fair Food Delivery

https://github.com/deeplearningbrasil/mars-gym

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MARS-Gym: A Gym framework to model, train, and evaluate Recommender Systems for Marketplaces

https://github.com/deepmind/jax_privacy

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Unlocking Accuracy and Fairness in Differentially Private Image Classification

https://github.com/deepmind/wasserstein_fairness

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Wasserstein Fair Classification

https://github.com/demeulemeestert/rawlsian-assignments

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Rawlsian Assignments

https://github.com/devanshgupta160/stochastic-differentially-private-and-fair-learning

공식 코드

Stochastic Differentially Private and Fair Learning

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