Papers Fairness
“Fairness” 태그가 달린 논문 5,676편 · 필터 해제
A Reproducibility Study of Product-side Fairness in Bundle Recommendation
Recommender systems are known to exhibit fairness issues, particularly on the product side, where products and their associated suppliers receive unequal exposure in recommended results. While this problem has been widel…
FairnessRecommendation SystemsFedGA: A Fair Federated Learning Framework Based on the Gini Coefficient
Fairness has emerged as one of the key challenges in federated learning. In horizontal federated settings, data heterogeneity often leads to substantial performance disparities across clients, raising concerns about equi…
FairnessFederated LearningLooking for Fairness in Recommender Systems
Recommender systems can be found everywhere today, shaping our everyday experience whenever we're consuming content, ordering food, buying groceries online, or even just reading the news. Let's imagine we're in the proce…
FairnessRecommendation SystemsFADE: Adversarial Concept Erasure in Flow Models
Diffusion models have demonstrated remarkable image generation capabilities, but also pose risks in privacy and fairness by memorizing sensitive concepts or perpetuating biases. We propose a novel \textbf{concept erasure…
FairnessImage GenerationFairness-Aware Grouping for Continuous Sensitive Variables: Application for Debiasing Face Analysis with respect to Skin Tone
Within a legal framework, fairness in datasets and models is typically assessed by dividing observations into predefined groups and then computing fairness measures (e.g., Disparate Impact or Equality of Odds with respec…
FairnessGuiding LLM Decision-Making with Fairness Reward Models
Large language models are increasingly used to support high-stakes decisions, potentially influencing who is granted bail or receives a loan. Naive chain-of-thought sampling can improve average decision accuracy, but has…
Decision MakingFairnessFairness-Aware Secure Integrated Sensing and Communications with Fractional Programming
We propose a novel secure integrated sensing and communications (ISAC) system designed to serve multiple communication users (CUs) and targets. To that end, we formulate an optimization problem that maximizes the secrecy…
FairnessISACMeta-Reinforcement Learning for Fast and Data-Efficient Spectrum Allocation in Dynamic Wireless Networks
The dynamic allocation of spectrum in 5G / 6G networks is critical to efficient resource utilization. However, applying traditional deep reinforcement learning (DRL) is often infeasible due to its immense sample complexi…
Deep Reinforcement LearningFairnessMeta-LearningMeta Reinforcement LearningFairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening
The increasing use of generative AI for resume screening is predicated on the assumption that it offers an unbiased alternative to biased human decision-making. However, this belief fails to address a critical question: …
FairnessTowards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift
Collaborative fairness is a crucial challenge in federated learning. However, existing approaches often overlook a practical yet complex form of heterogeneity: imbalanced covariate shift. We provide a theoretical analysi…
Collaborative FairnessFairnessFederated LearningKnowledge DistillationGraph Learning
Graph learning has rapidly evolved into a critical subfield of machine learning and artificial intelligence (AI). Its development began with early graph-theoretic methods, gaining significant momentum with the advent of …
Drug DiscoveryFairnessFraud DetectionGraph Learning+2LumiCRS: Asymmetric Contrastive Prototype Learning for Long-Tail Conversational Movie Recommendation
Conversational recommender systems (CRSs) often suffer from an extreme long-tail distribution of dialogue data, causing a strong bias toward head-frequency blockbusters that sacrifices diversity and exacerbates the cold-…
DiversityFairnessInformativenessMovie Recommendation+1BiFair: A Fairness-aware Training Framework for LLM-enhanced Recommender Systems via Bi-level Optimization
Large Language Model-enhanced Recommender Systems (LLM-enhanced RSs) have emerged as a powerful approach to improving recommendation quality by leveraging LLMs to generate item representations. Despite these advancements…
FairnessLarge Language ModelRecommendation SystemsIndianBailJudgments-1200: A Multi-Attribute Dataset for Legal NLP on Indian Bail Orders
Legal NLP remains underdeveloped in regions like India due to the scarcity of structured datasets. We introduce IndianBailJudgments-1200, a new benchmark dataset comprising 1200 Indian court judgments on bail decisions, …
AttributeFairnessJurisprudenceLegal ReasoningIs Reasoning All You Need? Probing Bias in the Age of Reasoning Language Models
Reasoning Language Models (RLMs) have gained traction for their ability to perform complex, multi-step reasoning tasks through mechanisms such as Chain-of-Thought (CoT) prompting or fine-tuned reasoning traces. While the…
Adversarial RobustnessAllFairnessMateInfoUB: A Real-World Benchmark for Testing LLMs in Competitive, Multilingual, and Multimodal Educational Tasks
The rapid advancement of Large Language Models (LLMs) has transformed various domains, particularly computer science (CS) education. These models exhibit remarkable capabilities in code-related tasks and problem-solving,…
FairnessMultiple-choiceEvaluating the Promise and Pitfalls of LLMs in Hiring Decisions
The use of large language models (LLMs) in hiring promises to streamline candidate screening, but it also raises serious concerns regarding accuracy and algorithmic bias where sufficient safeguards are not in place. In t…
FairnessWhy Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System
Unfairness is a well-known challenge in Recommender Systems (RSs), often resulting in biased outcomes that disadvantage users or items based on attributes such as gender, race, age, or popularity. Although some approache…
Contrastive LearningFairnessRecommendation SystemsDatasets for Fairness in Language Models: An In-Depth Survey
Fairness benchmarks play a central role in shaping how we evaluate language models, yet surprisingly little attention has been given to examining the datasets that these benchmarks rely on. This survey addresses that gap…
FairnessArtificial Delegates Resolve Fairness Issues in Perpetual Voting with Partial Turnout
Perpetual voting addresses fairness in sequential collective decision-making by evaluating representational equity over time. However, existing perpetual voting rules rely on full participation and complete approval info…
Decision MakingFairness