https://github.com/silkehusse/re-evaluating-bias
공식 코드Mind Your Bias: A Critical Review of Bias Detection Methods for Contextual Language Models
Papers with Code 아카이브
Bias Detection
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Bias detection is the task of detecting and measuring racism, sexism and otherwise discriminatory behavior in a model (Source: https://stereoset.mit.edu/)
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/snfrieder/ghosts
공식 코드https://github.com/songjinbo/ECMM
공식 코드Rethinking Large-scale Pre-ranking System: Entire-chain Cross-domain Models
https://github.com/spaidataiga/redditpoliticalbias
공식 코드https://github.com/sunxin000/uncertainty4recsys
공식 코드Uncertainty Calibration for Counterfactual Propensity Estimation in Recommendation
https://github.com/superpig99/SBRL-HAP
공식 코드Stable Heterogeneous Treatment Effect Estimation across Out-of-Distribution Populations
https://github.com/tagas/bQCD
공식 코드Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery
https://github.com/tanganke/hmr
공식 코드https://github.com/tanvidadu/Subjective-Bias-Detection
공식 코드Towards Detection of Subjective Bias using Contextualized Word Embeddings
https://github.com/thomassutter/mvhg
공식 코드Learning Group Importance using the Differentiable Hypergeometric Distribution
https://github.com/thu-bpm/gradlre
공식 코드Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction
https://github.com/tonyhrule/queer-bias-llms
공식 코드Mitigating Bias in Queer Representation within Large Language Models: A Collaborative Agent Approach
https://github.com/tosingithub/bipol
공식 코드Bipol: Multi-axes Evaluation of Bias with Explainability in Benchmark Datasets
https://github.com/tydpan/gpc
공식 코드Pre-Training LiDAR-Based 3D Object Detectors Through Colorization
https://github.com/uclanlp/clusters
공식 코드https://github.com/usaito/asymmetric-tri-rec-real
공식 코드Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback
https://github.com/usaito/ijcai2022-adversarial-mf
공식 코드Towards Resolving Propensity Contradiction in Offline Recommender Learning
https://github.com/valbarriere/biases_ppl
공식 코드https://github.com/vanderschaarlab/mlforhealthlabpub/tree/main/alg/causal_multitask_gaussian_processes_ite
공식 코드Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes
https://github.com/vdenberg/context-in-informational-bias-detection
공식 코드https://github.com/vergrig/RuBia-Dataset
공식 코드https://github.com/vila-lab/open-llm-leaderboard
공식 코드https://github.com/vsemenova/leebounds
공식 코드https://github.com/vshirvaikar/recidivism
공식 코드https://github.com/wallacesui/kdd25-background-variable
공식 코드Addressing Correlated Latent Exogenous Variables in Debiased Recommender Systems
https://github.com/wangxieric/bias-crs
공식 코드https://github.com/washuvis/vis2022usermodels
공식 코드https://github.com/waterdisappear/data-bias-in-mstar
공식 코드Discovering and Explaining the Non-Causality of Deep Learning in SAR ATR
https://github.com/waterdisappear/data-selection-in-mstar
공식 코드Discovering and Explaining the Non-Causality of Deep Learning in SAR ATR
https://github.com/wawrzenczyka/VP-Bayes-S
공식 코드Augmented prediction of a true class for Positive Unlabeled data under selection bias
https://github.com/webis-de/EMNLP-20
공식 코드Detecting Media Bias in News Articles using Gaussian Bias Distributions
https://github.com/weiguowilliam/CEAT
공식 코드https://github.com/wonbinkweon/dce_www2024
공식 코드Doubly Calibrated Estimator for Recommendation on Data Missing Not At Random
https://github.com/wwbp/robust-poststratification
공식 코드Correcting Sociodemographic Selection Biases for Population Prediction from Social Media
https://github.com/xyfrieder/science-ghosts
공식 코드https://github.com/yansuoyuli/reform
공식 코드RE-SORT: Removing Spurious Correlation in Multilevel Interaction for CTR Prediction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.