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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/educational-technology-collective/cross-institutional-transfer-learning-facct-2023

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Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and Equity

https://github.com/ehyaei/fair-robust-algorithmic-resource

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Robustness Implies Fairness in Causal Algorithmic Recourse

https://github.com/ehyaei/robustness-implies-fairness

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Robustness Implies Fairness in Causal Algorithmic Recourse

https://github.com/eilamshapira/GLEE

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GLEE: A Unified Framework and Benchmark for Language-based Economic Environments

https://github.com/ekgus9/FLEX

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FLEX: A Benchmark for Evaluating Robustness of Fairness in Large Language Models

https://github.com/eleutherai/concept-erasure

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LEACE: Perfect linear concept erasure in closed form

https://github.com/eliacunegatti/moeim

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Many-Objective Evolutionary Influence Maximization: Balancing Spread, Budget, Fairness, and Time

https://github.com/eliasbenussi/nn-cert-individual-fairness

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Individual Fairness Guarantees for Neural Networks

https://github.com/ellenxtan/rise

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RISE: Robust Individualized Decision Learning with Sensitive Variables

https://github.com/elviswxy/LVLM_fairness

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Evaluating Fairness in Large Vision-Language Models Across Diverse Demographic Attributes and Prompts

https://github.com/elviswxy/rag_fairness

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Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation Systems

https://github.com/emilbjornson/power-allocation-cell-free

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Learning-Based Downlink Power Allocation in Cell-Free Massive MIMO Systems

https://github.com/emiliaagis/differences-in-fairness-preferences-acl-2024

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Whose Preferences? Differences in Fairness Preferences and Their Impact on the Fairness of AI Utilizing Human Feedback

https://github.com/enscma2/fairest-mixup

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Who's the (Multi-)Fairest of Them All: Rethinking Interpolation-Based Data Augmentation Through the Lens of Multicalibration

https://github.com/ericotjo001/srd

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Self Reward Design with Fine-grained Interpretability

https://github.com/ericyangyu/pocar

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Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems

https://github.com/ericzhao28/multidistributionlearning

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On-Demand Sampling: Learning Optimally from Multiple Distributions

https://github.com/eringrant/icml-2022-rules-vs-exemplars

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Distinguishing rule- and exemplar-based generalization in learning systems

https://github.com/ermongroup/lag-fairness

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Learning Controllable Fair Representations

https://github.com/erwanlemerrer/bouncer_problem

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The Bouncer Problem: Challenges to Remote Explainability

https://github.com/eslambakr/HRS_benchmark

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HRS-Bench: Holistic, Reliable and Scalable Benchmark for Text-to-Image Models

https://github.com/essdeee/Ensuring-Fairness-Beyond-the-Training-Data

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Ensuring Fairness Beyond the Training Data

https://github.com/essdeee/FairnessChecking

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Ensuring Fairness Beyond the Training Data

https://github.com/estherrolf/multi-objective-impact

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Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning

https://github.com/estija/cmid

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Mitigating Simplicity Bias in Deep Learning for Improved OOD Generalization and Robustness

https://github.com/eternityyw/biaseval-llm-mentalhealth

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Unveiling and Mitigating Bias in Mental Health Analysis with Large Language Models

https://github.com/eth-sri/cuts

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CuTS: Customizable Tabular Synthetic Data Generation

https://github.com/eth-sri/fairness-feedback-nlp

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Human-Guided Fair Classification for Natural Language Processing

https://github.com/eth-sri/fare

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FARE: Provably Fair Representation Learning with Practical Certificates

https://github.com/eth-sri/fnf

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Fair Normalizing Flows

https://github.com/eth-sri/lassi

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Latent Space Smoothing for Individually Fair Representations

https://github.com/eth-sri/lcifr

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Learning Certified Individually Fair Representations

https://github.com/eth-sri/phoenix

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Private and Reliable Neural Network Inference

https://github.com/ethanlq/scma-codebook

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Advanced Codebook Design for SCMA-aided NTNs With Randomly Distributed Users

https://github.com/euranova/calibrate_to_interpret

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Calibrate to Interpret

https://github.com/eustomaqua/ApproxBias

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Does Machine Bring in Extra Bias in Learning? Approximating Fairness in Models Promptly

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