Papers with Code 아카이브

Bias Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 1
평가표: 5
보존된 평가 기록: 19

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://bitbucket.org/mvdschaar/mlforhealthlabpub

공식 코드

Learning Overlapping Representations for the Estimation of Individualized Treatment Effects

https://bitbucket.org/robvanderg/w2v

공식 코드

Fair is Better than Sensational:Man is to Doctor as Woman is to Doctor

https://github.com/2dot71mily/sib_paper

공식 코드

Underspecification in Language Modeling Tasks: A Causality-Informed Study of Gendered Pronoun Resolution

https://github.com/2dot71mily/spurious_correlations_icml_2022

공식 코드

Selection Bias Induced Spurious Correlations in Large Language Models

https://github.com/2dot71mily/uspec

공식 코드

Underspecification in Language Modeling Tasks: A Causality-Informed Study of Gendered Pronoun Resolution

https://github.com/A-bone1/ESAM

공식 코드

ESAM: Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance

https://github.com/Amaodemao/BiasPainter

공식 코드

New Job, New Gender? Measuring the Social Bias in Image Generation Models

https://github.com/BFH-AMI/BIAS

공식 코드

The BIAS Detection Framework: Bias Detection in Word Embeddings and Language Models for European Languages

https://github.com/CSHaitao/CalibraEval

공식 코드

CalibraEval: Calibrating Prediction Distribution to Mitigate Selection Bias in LLMs-as-Judges

https://github.com/DanielNgo207/Incentivizing-Compliance-with-Algorithmic-Instruments

공식 코드

Incentivizing Compliance with Algorithmic Instruments

https://github.com/FabianObster/sgboost-introduction

공식 코드

Sparse-Group Boosting with Balanced Selection Frequencies: A Simulation-Based Approach and R Implementation

https://github.com/H-shw/futurepedia

공식 코드

Not All Languages are Equal: Insights into Multilingual Retrieval-Augmented Generation

https://github.com/HarrieO/2020ictir-evaluation

공식 코드

Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking

https://github.com/HarrieO/2021wsdm-unifying-LTR

공식 코드

Unifying Online and Counterfactual Learning to Rank

https://github.com/HarrieO/OnlineLearningToRank

공식 코드

To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions

https://github.com/IngrojShrestha/robust_mlm_bias_detection_human_trait_ratings

공식 코드

Robust Bias Detection in MLMs and its Application to Human Trait Ratings

https://github.com/IntelLabs/causality-lab

공식 코드

Iterative Causal Discovery in the Possible Presence of Latent Confounders and Selection Bias

https://github.com/Ipsumio/plantvillage_bias

공식 코드

Uncovering bias in the PlantVillage dataset

https://github.com/Jo-Lam/Jyutping_project

공식 코드

Decolonising Data Systems: Using Jyutping or Pinyin as tonal representations of Chinese names for data linkage

https://github.com/KMnP/vise

공식 코드

Exploring Visual Engagement Signals for Representation Learning

https://github.com/KimYenny/HeterogeneousRF

공식 코드

Heterogeneous Random Forest

https://github.com/LCS2-IIITD/summarization_bias

공식 코드

Corpora Evaluation and System Bias Detection in Multi-document Summarization

https://github.com/MarkDana/CDIS

공식 코드

When Selection Meets Intervention: Additional Complexities in Causal Discovery

https://github.com/Media-Bias-Group/llm-annotations-annomatic

공식 코드

The Promises and Pitfalls of LLM Annotations in Dataset Labeling: a Case Study on Media Bias Detection

https://github.com/MichiganNLP/MultiAgent_ImplicitBias

공식 코드

Towards Implicit Bias Detection and Mitigation in Multi-Agent LLM Interactions

https://github.com/Powertony102/corn_official

공식 코드

Leveraging CORAL-Correlation Consistency Network for Semi-Supervised Left Atrium MRI Segmentation

https://github.com/RyanWangZf/SurvTRACE

공식 코드

SurvTRACE: Transformers for Survival Analysis with Competing Events

https://github.com/TIA-Lab/PanNuke-metrics

공식 코드

PanNuke Dataset Extension, Insights and Baselines

https://github.com/TURuibo/Neuropathic-Pain-Diagnosis-Simulator

공식 코드

Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation

https://github.com/agamiko/gebi

공식 코드

Towards explainable classifiers using the counterfactual approach -- global explanations for discovering bias in data

https://github.com/akoksal/labdet

공식 코드

Language-Agnostic Bias Detection in Language Models with Bias Probing

https://github.com/alishiraligit/eval-on-laion

공식 코드

What Makes ImageNet Look Unlike LAION

https://github.com/amazon-science/multi-way-parallel-ccmatrix

공식 코드

A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelism

https://github.com/amazon-science/synthesizrr

공식 코드

SynthesizRR: Generating Diverse Datasets with Retrieval Augmentation

https://github.com/ambroiseodt/tsim

공식 코드

Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection Bias

https://github.com/arthua196/Leakage-Neutral-Learning-for-QuoraQP

공식 코드

Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets

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