Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution | Paper Citation Record

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution write newline

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Unresolved cited work

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This paper cites Do Large Language Models Discriminate in Hiring Decisions on the Basis of Race, Ethnicity, and Gender?.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Do Large Language Models Discriminate in Hiring Decisions on the Basis of Race, Ethnicity, and Gender?

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This paper cites The Silicon Ceiling: Auditing GPT's Race and Gender Biases in Hiring.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution The Silicon Ceiling: Auditing GPT's Race and Gender Biases in Hiring

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This paper cites RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models

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This paper cites Unmasking Contextual Stereotypes: Measuring and Mitigating BERT's Gender Bias.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Unmasking Contextual Stereotypes: Measuring and Mitigating BERT's Gender Bias

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This paper cites On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pp.\ 610--623, 2021.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pp.\ 610--623, 2021

Reference 7

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This paper cites Are emily and greg more employable than lakisha and jamal? a field experiment on labor market discrimination.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Are emily and greg more employable than lakisha and jamal? a field experiment on labor market discrimination

Reference 8

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This paper cites Language (technology) is power: A critical survey of `` bias '' in NLP.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Language (technology) is power: A critical survey of `` bias '' in NLP

Reference 9

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This paper cites Stereotyping norwegian salmon: An inventory of pitfalls in fairness benchmark datasets.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Stereotyping norwegian salmon: An inventory of pitfalls in fairness benchmark datasets

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This paper cites Language and identity.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Language and identity

Reference 11

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This paper cites Toward gender-inclusive coreference resolution: An analysis of gender and bias throughout the machine learning lifecycle.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Toward gender-inclusive coreference resolution: An analysis of gender and bias throughout the machine learning lifecycle

Reference 12

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This paper cites Extracting intersectional stereotypes from embeddings: Developing and validating the flexible intersectional stereotype extraction procedure.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Extracting intersectional stereotypes from embeddings: Developing and validating the flexible intersectional stereotype extraction procedure

Reference 13

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This paper cites Intersectionality as critical social theory: Intersectionality as critical social theory, patricia hill collins, duke university press, 2019.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Intersectionality as critical social theory: Intersectionality as critical social theory, patricia hill collins, duke university press, 2019

Reference 14

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This paper cites A validity perspective on evaluating the justified use of data-driven decision-making algorithms.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution A validity perspective on evaluating the justified use of data-driven decision-making algorithms

Reference 15

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This paper cites The algorithmic leviathan: Arbitrariness, fairness, and opportunity in algorithmic decision-making systems.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution The algorithmic leviathan: Arbitrariness, fairness, and opportunity in algorithmic decision-making systems

Reference 16

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This paper cites Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics

Reference 17

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This paper cites Are ai systems biased against the poor? a machine learning analysis using word2vec and glove embeddings.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Are ai systems biased against the poor? a machine learning analysis using word2vec and glove embeddings

Reference 18

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This paper cites Second Order WinoBias (SoWinoBias) Test Set for Latent Gender Bias Detection in Coreference Resolution.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Second Order WinoBias (SoWinoBias) Test Set for Latent Gender Bias Detection in Coreference Resolution

Reference 19

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This paper cites Decomposition of uncertainty in bayesian deep learning for efficient and risk-sensitive learning.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Decomposition of uncertainty in bayesian deep learning for efficient and risk-sensitive learning

Reference 20

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This paper cites On measuring and mitigating biased inferences of word embeddings.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution On measuring and mitigating biased inferences of word embeddings

Reference 21

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This paper cites Fairness through awareness.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Fairness through awareness

Reference 22

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution WinoQueer: A Community-in-the-Loop Benchmark for Anti-LGBTQ+ Bias in Large Language Models

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution A model of (often mixed) stereotype content: Competence and warmth respectively follow from perceived status and competition

Reference 24

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Dropout as a bayesian approximation: Representing model uncertainty in deep learning

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Counterfactual fairness in text classification through robustness

Reference 26

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This paper cites A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challenges.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challenges

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Algorithmic arbitrariness in content moderation

Reference 28

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This paper cites Akal badi ya bias: An exploratory study of gender bias in hindi language technology.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Akal badi ya bias: An exploratory study of gender bias in hindi language technology

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Equality of opportunity in supervised learning

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution MISGENDERED: Limits of Large Language Models in Understanding Pronouns

Reference 31

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Socialcounterfactuals: Probing and mitigating intersectional social biases in vision-language models with counterfactual examples

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods

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This paper cites Are female carpenters like blue bananas? a corpus investigation of occupation gender typicality.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Are female carpenters like blue bananas? a corpus investigation of occupation gender typicality

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Taxonomizing and measuring representational harms: A look at image tagging

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution What uncertainties do we need in bayesian deep learning for computer vision? In I

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Dreyer, Aleksandar Shtedritski, and Yuki M

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Stereotype content at the intersection of gender and sexual orientation

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution intersectionally fair

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Gender bias and stereotypes in large language models

Reference 41

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Uncertainty as a fairness measure

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Simple and scalable predictive uncertainty estimation using deep ensembles

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution A Survey on Fairness in Large Language Models

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This paper cites Comparing diversity, negativity, and stereotypes in Chinese-language AI technologies: an investigation of Baidu, Ernie and Qwen.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Comparing diversity, negativity, and stereotypes in Chinese-language AI technologies: an investigation of Baidu, Ernie and Qwen

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This paper cites Intersectional stereotypes in large language models: Dataset and analysis.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Intersectional stereotypes in large language models: Dataset and analysis

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This paper cites GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Torr, and Yarin Gal

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This paper cites StereoSet: Measuring stereotypical bias in pretrained language models.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution StereoSet: Measuring stereotypical bias in pretrained language models

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Factoring the matrix of domination: A critical review and reimagination of intersectionality in ai fairness

Reference 52

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This paper cites BBQ : A hand-built bias benchmark for question answering.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution BBQ : A hand-built bias benchmark for question answering

Reference 53

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Perturbation Augmentation for Fairer NLP

Reference 54

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This paper cites Gender Bias in Coreference Resolution.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Gender Bias in Coreference Resolution

Reference 55

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution The unequal opportunities of large language models: Examining demographic biases in job recommendations by chatgpt and llama

Reference 56

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This paper cites Are emergent abilities of large language models a mirage? Advances in Neural Information Processing Systems, 36: 0 55565--55581, 2023.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Are emergent abilities of large language models a mirage? Advances in Neural Information Processing Systems, 36: 0 55565--55581, 2023

Reference 57

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution The Woman Worked as a Babysitter: On Biases in Language Generation

Reference 58

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution A framework for understanding sources of harm throughout the machine learning life cycle

Reference 59

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Fairness through aleatoric uncertainty

Reference 60

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender-Neutral Alternatives

Reference 61

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Measuring representational harms in image captioning

Reference 62

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Aleatoric and epistemic discrimination: Fundamental limits of fairness interventions

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:21:06.717003Z

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Mind the gap: A balanced corpus of gendered ambiguous pronouns

Reference 64

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Easy Problems That LLMs Get Wrong

Reference 65

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Gender, race, and intersectional bias in resume screening via language model retrieval

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:21:06.694477Z

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Observation b11b55ae-0ea8-4453-ae46-ef981db1ae2b · outbound

This paper cites What is your favorite gender, mlm? gender bias evaluation in multilingual masked language models.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution What is your favorite gender, mlm? gender bias evaluation in multilingual masked language models

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:21:06.681586Z

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Observation 46ca3200-d5cb-441f-879c-d3925e80e69a · outbound

This paper cites Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:21:06.669718Z

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Gender bias in coreference resolution: Evaluation and debiasing methods

Reference 69

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This paper cites @esa (Ref.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution @esa (Ref

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-05T22:21:05.949668Z

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Unresolved cited work

Reference 71

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Observation 66125248-52c8-4f55-8d38-528d57ae7318 · outbound

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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution [pronoun]

Reference 72

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