Good Papers

Showing Fairness & bias Show all papers

88%Must read
?Must readVote to see the score
arXivFairness & bias

Noise Out, Bias In: Targeted Bias Injection in Diffusion Language Models via Closed-Loop Activation Steering

Targeted bias injection via closed-loop activation steering exploits diffusion language model denoising trajectories to steer frozen models toward adversarial demographic answers with minimal corruption.

Sarim Hashmi, Mukul Ranjan, Abdelrahman Elsayed, Muhammad Umer Sheikh and 2 more

Published Oct 5, 2026 · ▲ 14 on Hugging Face · Code ★ 3

– ReadersNo votes yet
15/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 15 of 20 reviewers recommend it
lenient 5/5
medium 9/10
strict 1/5
88%Must read
?Must readVote to see the score
arXivFairness & bias

FairRSFM: A Biome-Aware Benchmark and Debiasing Framework for Remote Sensing Foundation Models

FairRSFM benchmarks remote sensing foundation models by biome to expose hidden ecological performance disparities and tests debiasing methods without backbone updates. Aggregate metrics consistently mask large biome-dependent gaps, though mitigation effectiveness varies by model and task.

Md Aminur Hossain, Omkumar Vaghasiya, Rajeev Ranjan Dwivedi, Vinod Kurmi and 1 more

Published Oct 5, 2026 · ▲ 5 on Hugging Face · Code

– ReadersNo votes yet
15/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 15 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 2/5
74%Highly rated
?Highly ratedVote to see the score
arXivFairness & bias

Collective Bias Mitigation via Model Routing and Collaboration

Collective Bias Mitigation routes queries among diverse LLMs and fosters collaboration to substantially reduce bias over single-model baselines.

Mingzhe Du, Luu Anh Tuan, Xiaobao Wu, Yichong Huang and 6 more

Published Oct 2, 2026 · 0 citations · ▲ 14 on Hugging Face

– ReadersNo votes yet
9/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 9 of 20 reviewers recommend it
lenient 5/5
medium 4/10
strict 0/5
71%Highly rated
?Highly ratedVote to see the score
ICLR 2024U VirginiaIBMXi'an JiaotongFairness & bias

Adversarial Attacks on Fairness of Graph Neural Networks

G-FairAttack is a framework that adversarially corrupts fairness in fairness-aware graph neural networks while preserving prediction utility.

Binchi Zhang, Yushun Dong, Chen Chen, Yada Zhu and 2 more

Published Oct 20, 2023 · 1 citation · ▲ 1 on Hugging Face · Code ★ 11

– ReadersNo votes yet
7/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 7 of 20 reviewers recommend it
lenient 4/5
medium 3/10
strict 0/5
74%Highly rated
?Highly ratedVote to see the score
ACL 2023U WashingtonCarnegie MellonXi'an JiaotongFairness & bias

From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models

Pretraining data political biases propagate through language models into unfair hate speech and misinformation detectors, reinforcing polarization.

Shangbin Feng, Chan Young Park, Yuhan Liu, Yulia Tsvetkov

Published 2023 · 138 citations

– ReadersNo votes yet
9/21 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 9 of 21 reviewers recommend it
lenient 5/5
medium 4/11
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026U TennesseeFairness & bias

TailGuard: Subgroup Tail Coverage Theory for Safe LLM Alignment

Charles Cao

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026Fujitsu LimitedInstitute of Science TokyoFujitsu Ltd.Fairness & bias

Distributionally Robust Algorithmic Recourse for Tree Ensembles

Kentaro Kanamori, Ken Kobayashi, Takuya Takagi

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026Preferred NetworksRecruit. Co., Ltd.HoseiU TsukubaFairness & bias

Compact Representations of Impact-Based Fair-Ranking Policies

Yuki Uehara, Naoki Nishimura, Noriyoshi Sukegawa, Yuichi Takano

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026StanfordAppleU OxfordU Texas at AustinHarvardFairness & bias

Multigroup Fairness and Omniprediction: Separations and Equivalences

Sílvia Casacuberta, Parikshit Gopalan, Varun Kanade, Omer Reingold and 2 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
69%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026U Science and Technology of ChinSingapore University of TechnoloCapital MedicalBeijing JiaotongFairness & bias

Text-Based AI Tools for Research Integrity Must Be Audited on Linguistic Fairness Before Deployment

Shuai Shao, Yongkang Wan, Daoyin Dang, Lanyun Zhu and 4 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
3/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 3 of 20 reviewers recommend it
lenient 2/5
medium 1/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026U WashingtonBoson AI - we are hiring!Fairness & bias

The Pok\'emon Theorem and other Fairness Impossibility Results

Daniel Matsui Smola, Alexander Smola

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 1/5
67%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026Missouri S&TMissouri University of Science aFairness & bias

An Information-theoretic Framework for Auditing Unfairness in Training Data

Mohamed Nafea, Sokrat Aldarmini

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
2/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 2 of 20 reviewers recommend it
lenient 1/5
medium 1/10
strict 0/5
67%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026National University of SingaporeUC BerkeleyNUS, CMUFairness & bias

Treat Bias as Noise: Training Bias-Robust LLM Reasoning via Reinforcement Learning

Qian Wang, Xuandong Zhao, Zirui Zhang, Zhanzhi Lou and 3 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
2/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 2 of 20 reviewers recommend it
lenient 1/5
medium 1/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026U Illinois at Urbana-ChampaignUIUCInfocomm Media Development AuthoMedia Development AuthorityUIUC/Virtue AIFairness & bias

CultureRed: Benchmarking Culture-Specific AI Safety Based on Global Statutes

Xun Liu, Mintong Kang, Seok Min Lim, Ong C Hui and 1 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026Johns Hopkins UniveristyNanyang TechnologicalFairness & bias

New Coresets for Fair Clustering

Xuan Wu, Chansophea Wathanak In, Yi Li

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026PekingU California, BerkeleyMeituanBeihangInstitute of automation, ChineseFairness & bias

On the Bias of Group-Based Advantage Estimation

Fengkai Yang, Zherui Chen, Xiaohan Wang, Xiaodong Lu and 8 more

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026CENTAI, TUGrazCENTAISapienza University of Rome, CENIntesa Sanpaolo S.p.A.CentaiFairness & bias

Overcoming the Resolution Limit: Significance-Aware Regularization for Intersectional Fairness

Antonio Ferrara, André Panisson, Francesco Cozzi, Alan Perotti and 1 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026Soochow universitySoochow University, ChinaSoochowFairness & bias

Malicious Node Injection: A Transferable Adversarial Attack on GNN Fairness

Haotian Zhang, He Huang, Shuang Cui, Yu-e Sun

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026Tohoku University, Institute of RIKEN AIPMicrosoft ResearchRIKEN/University of TokyoRIKENFairness & bias

Practical Estimation of the Bayes Optimal Fairness-Accuracy Tradeoff with Soft Labels

mohit sharma, Okan Koc, Amit Jayant Deshpande, Takashi Ishida and 3 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026Georgia Institute of TechnologyThe Ohio StateOhio State University, ColumbusFairness & bias

FedCF: Fair Federated Conformal Prediction

Anutam Srinivasan, Aditya T. Vadlamani, Amin Meghrazi, Srinivasan Parthasarathy

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
Show 20 more papers