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Fairness

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코드

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

https://github.com/hkust-sing/astraea

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Towards Fair and Efficient Learning-based Congestion Control

https://github.com/hlt-mt/fbk-fairseq

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Test Suites Task: Evaluation of Gender Fairness in MT with MuST-SHE and INES

https://github.com/hltcoe/peer_measure

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Language Fairness in Multilingual Information Retrieval

https://github.com/holdenhu/advfm

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Automatic Feature Fairness in Recommendation via Adversaries

https://github.com/holistic-ai/SAGED-Bias

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SAGED: A Holistic Bias-Benchmarking Pipeline for Language Models with Customisable Fairness Calibration

https://github.com/homan-lab/crowdopinion

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Subjective Crowd Disagreements for Subjective Data: Uncovering Meaningful CrowdOpinion with Population-level Learning

https://github.com/hpcclab/Felare

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FELARE: Fair Scheduling of Machine Learning Tasks on Heterogeneous Edge Systems

https://github.com/hradghoukasian/dp_fair_binary

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Differentially Private Fair Binary Classifications

https://github.com/hsianghsu/fair-projection

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Beyond Adult and COMPAS: Fairness in Multi-Class Prediction

https://github.com/hsvgbkhgbv/shapley-q-learning

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SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning

https://github.com/huaishengzhu/fairws

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Learning Fair Models without Sensitive Attributes: A Generative Approach

https://github.com/huanglx12/Stable-Fair-Classification

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Stable and Fair Classification

https://github.com/huashen218/voxceleb-fairness

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Improving fairness in speaker verification via Group-adapted Fusion Network

https://github.com/huawei-noah/federated-learning

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Proportional Fairness in Federated Learning

https://github.com/hughvd/um-cai-fellowship

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From Interests to Insights: An LLM Approach to Course Recommendations Using Natural Language Queries

https://github.com/huihanlhh/culture-gen

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CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language Prompting

https://github.com/huihevv/fairfor

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Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspective

https://github.com/huihuichyan/unlimitedjudge

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An Empirical Study of LLM-as-a-Judge for LLM Evaluation: Fine-tuned Judge Model is not a General Substitute for GPT-4

https://github.com/human-analysis/tradeoff-invariant-representation-learning

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On Characterizing the Trade-off in Invariant Representation Learning

https://github.com/hwq0726/sdoh-kg

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Integrating Social Determinants of Health into Knowledge Graphs: Evaluating Prediction Bias and Fairness in Healthcare

https://github.com/hydroml/umfi

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Ultra-marginal Feature Importance: Learning from Data with Causal Guarantees

https://github.com/hyungrok-do/fair-glm-cvx

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Fair Generalized Linear Models with a Convex Penalty

https://github.com/hzhang0/auc_bias

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A Closer Look at AUROC and AUPRC under Class Imbalance

https://github.com/i-gallegos/fair-llm-benchmark

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Bias and Fairness in Large Language Models: A Survey

https://github.com/i6092467/diff-bias-proxies

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Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods

https://github.com/iPieter/RobBERT

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RobBERT: a Dutch RoBERTa-based Language Model

https://github.com/iPieter/ethical-adversaries

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Ethical Adversaries: Towards Mitigating Unfairness with Adversarial Machine Learning

https://github.com/iamjackyan/fed-lwr

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A New Perspective to Boost Performance Fairness for Medical Federated Learning

https://github.com/iamownt/TRUECAM

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Implementing Trust in Non-Small Cell Lung Cancer Diagnosis with a Conformalized Uncertainty-Aware AI Framework in Whole-Slide Images

https://github.com/iamtheo2000/musiclime

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MusicLIME: Explainable Multimodal Music Understanding

https://github.com/ibm/torchlogic

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Neural Reasoning Networks: Efficient Interpretable Neural Networks With Automatic Textual Explanations

https://github.com/ibsmirnov/debiasing

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Quota-based debiasing can decrease representation of already underrepresented groups

https://github.com/iclr2020-apd/anonymous_iclr2020_apd_code

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Scalable and Order-robust Continual Learning with Additive Parameter Decomposition

https://github.com/ideas-labo/fes

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Contexts Matter: An Empirical Study on Contextual Influence in Fairness Testing for Deep Learning Systems

https://github.com/idrave/aspplanner

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Flexible FOND Planning with Explicit Fairness Assumptions

https://github.com/ifzhang/FairMOT

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FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.