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Fairness

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

https://github.com/autogluon/autogluon-fair

Does mitigating ML's impact disparity require treatment disparity?

https://github.com/awslabs/autogluon

Multi-objective Asynchronous Successive Halving

https://github.com/ayong8/FairSight

FairSight: Visual Analytics for Fairness in Decision Making

https://github.com/baal-org/baal

Can Active Learning Preemptively Mitigate Fairness Issues?

https://github.com/baichuan-inc/Baichuan-Omni-1.5

Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution

https://github.com/balakg/transects-eccv2020

Towards causal benchmarking of bias in face analysis algorithms

https://github.com/bdemo/pfedbred_public

Federated Multi-Task Learning under a Mixture of Distributions

https://github.com/beandkay/epass

FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning

https://github.com/bhofstra/diversity_innovation_paradox

Predicting Race and Ethnicity From the Sequence of Characters in a Name

https://github.com/brandonhanx/compfashion

RTIC: Residual Learning for Text and Image Composition using Graph Convolutional Network

https://github.com/brcsomnath/coversumm

Enhancing Group Fairness in Online Settings Using Oblique Decision Forests

https://github.com/bspanjers/AIFBAREND

Mitigating Unwanted Biases with Adversarial Learning

https://github.com/bupt-gamma/uncovering-the-structural-fairness-in-graph-contrastive-learning

Uncovering the Structural Fairness in Graph Contrastive Learning

https://github.com/bytedance/tarsier

MVBench: A Comprehensive Multi-modal Video Understanding Benchmark

https://github.com/cambridgeltl/pairs

Fairer Preferences Elicit Improved Human-Aligned Large Language Model Judgments

https://github.com/carla-recourse/CARLA

Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems

https://github.com/carol-long/fairness_and_arbitrariness

Arbitrariness Lies Beyond the Fairness-Accuracy Frontier

https://github.com/cavalab/feat

Genetic programming approaches to learning fair classifiers

https://github.com/cds-mipt/cds-tracking

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

https://github.com/cesarojuliana/feature_importance_fairness

AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias

https://github.com/chandar-lab/healthy-data-diet

Deep Learning on a Healthy Data Diet: Finding Important Examples for Fairness

https://github.com/charan223/FairDeepLearning

A comparative study of fairness-enhancing interventions in machine learning

https://github.com/chen-si-jia/Trajectory-Long-tail-Distribution-for-MOT

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

https://github.com/chenzhenpeng18/fse22-maat

AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias

https://github.com/claimed-framework/component-library

CLAIMED, a visual and scalable component library for Trusted AI

https://github.com/codelion/adaptive-classifier

Group-Adaptive Threshold Optimization for Robust AI-Generated Text Detection

https://github.com/coinse/arachne

Arachne: Search Based Repair of Deep Neural Networks

https://github.com/congduytran12/FACROC-Experiments

FACROC: a fairness measure for FAir Clustering through ROC curves

https://github.com/cran/fairmodels

Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment

https://github.com/cteicher-m/loanBiases

On the (im)possibility of fairness

https://github.com/d3m-research-group/odtlearn

Learning Optimal Prescriptive Trees from Observational Data

https://github.com/dalejn/cleanBib

Predicting Race and Ethnicity From the Sequence of Characters in a Name

https://github.com/datalab-georgetown/fairness-and-missing-values

AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias

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

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

https://github.com/dhu-mmct/dhu-mmct

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

https://github.com/dingwoai/FairMOT-BDD100K

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.