Papers with Code 아카이브

Fairness

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코드

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

https://github.com/taosheng-ty/wsdm23-mcfair

공식 코드

Marginal-Certainty-aware Fair Ranking Algorithm

https://github.com/tapilab/emnlp-2021-regularization

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Enhancing Model Robustness and Fairness with Causality: A Regularization Approach

https://github.com/tatsu-lab/alpaca_farm

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Beyond Reward Hacking: Causal Rewards for Large Language Model Alignment

https://github.com/tayebiarasteh/dp_cxr

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Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging

https://github.com/tayebiarasteh/pathologyanonym

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The Impact of Speech Anonymization on Pathology and Its Limits

https://github.com/tayebiarasteh/privacydomain

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Preserving privacy in domain transfer of medical AI models comes at no performance costs: The integral role of differential privacy

https://github.com/tdavislab/verb

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VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representations

https://github.com/tdemin16/group-robust_machine_unlearning

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Group-robust Machine Unlearning

https://github.com/teddyzander/mcgif

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Equalised Odds is not Equal Individual Odds: Post-processing for Group and Individual Fairness

https://github.com/tensorflow/lattice

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Deontological Ethics By Monotonicity Shape Constraints

https://github.com/tensorflow/tensorboard

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Visual Identification of Problematic Bias in Large Label Spaces

https://github.com/tensorflow/tensorflow

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Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals

https://github.com/teresalazar13/Survey-Group-Fairness-in-Federated-Learning

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A Survey on Group Fairness in Federated Learning: Challenges, Taxonomy of Solutions and Directions for Future Research

https://github.com/teresalazar13/fair-fate

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FAIR-FATE: Fair Federated Learning with Momentum

https://github.com/teresalazar13/fairfeddrift

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Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning

https://github.com/terne/being_right_for_whose_right_reasons

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Being Right for Whose Right Reasons?

https://github.com/ternencewu123/gautomri

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Generalizable Learning Reconstruction for Accelerating MR Imaging via Federated Neural Architecture Search

https://github.com/tess022095/fair-automl

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Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoML

https://github.com/textshuttle/exploiting-bias-to-debias

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Exploiting Biased Models to De-bias Text: A Gender-Fair Rewriting Model

https://github.com/thaleaschlender/An-Evaluation-of-Multiclass-Debiasing-Methods-on-Word-Embeddings

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"Thy algorithm shalt not bear false witness": An Evaluation of Multiclass Debiasing Methods on Word Embeddings

https://github.com/that-recsys-lab/scruf_facctrec_2023

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Exploring Social Choice Mechanisms for Recommendation Fairness in SCRUF

https://github.com/the-chen-lab/data-addition-dilemma

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The Data Addition Dilemma

https://github.com/theNamek/Bias-Neurons

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The Devil is in the Neurons: Interpreting and Mitigating Social Biases in Pre-trained Language Models

https://github.com/theaperdeng/gnn-generalization-fairness

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Subgroup Generalization and Fairness of Graph Neural Networks

https://github.com/thegoose20/gender-bias

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Investigating the Capabilities and Limitations of Machine Learning for Identifying Bias in English Language Data with Information and Heritage Professionals

https://github.com/theresiavr/DPFR-recsys-evaluation

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Joint Evaluation of Fairness and Relevance in Recommender Systems with Pareto Frontier

https://github.com/theresiavr/can-we-trust-recsys-fairness-evaluation

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Can We Trust Recommender System Fairness Evaluation? The Role of Fairness and Relevance

https://github.com/theresiavr/individual-item-fairness-measures-recsys

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Evaluation Measures of Individual Item Fairness for Recommender Systems: A Critical Study

https://github.com/thieu1995/mealpy

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A Prescription of Methodological Guidelines for Comparing Bio-inspired Optimization Algorithms

https://github.com/thomaspzollo/dhacking

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Towards Effective Discrimination Testing for Generative AI

https://github.com/throwaway20190523/MonotonicFairness

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Avoiding Resentment Via Monotonic Fairness

https://github.com/thulawtech/muser

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MUSER: A Multi-View Similar Case Retrieval Dataset

https://github.com/thunlp/lead

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Enhancing Legal Case Retrieval via Scaling High-quality Synthetic Query-Candidate Pairs

https://github.com/thunlp/readin

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READIN: A Chinese Multi-Task Benchmark with Realistic and Diverse Input Noises

https://github.com/timelovercc/awesome-graph-causal-learning

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Counterfactual Learning on Graphs: A Survey

https://github.com/timelovercc/caf-gnn

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Towards Fair Graph Neural Networks via Graph Counterfactual

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