Tootfinder

On Fair Epsilon Net and Geometric Hitting Set
Mohsen Dehghankar, Stavros Sintos, Abolfazl Asudeh
https://arxiv.org/abs/2507.08758 https://

On Fair Epsilon Net and Geometric Hitting Set
Fairness has emerged as a formidable challenge in data-driven decisions. Many of the data problems, such as creating compact data summaries for approximate query processing, can be effectively tackled using concepts from computational geometry, such as $\varepsilon$-nets. However, these powerful tools have yet to be examined from the perspective of fairness. To fill this research gap, we add fairness to classical geometric approximation problems of $\varepsilon$-net, $\varepsilon$-sample, and g…

Fair Domain Generalization: An Information-Theoretic View
Tangzheng Lian, Guanyu Hu, Dimitrios Kollias, Xinyu Yang, Oya Celiktutan
https://arxiv.org/abs/2507.05823

Fair Domain Generalization: An Information-Theoretic View
Domain generalization (DG) and algorithmic fairness are two critical challenges in machine learning. However, most DG methods focus only on minimizing expected risk in the unseen target domain without considering algorithmic fairness. Conversely, fairness methods typically do not account for domain shifts, so the fairness achieved during training may not generalize to unseen test domains. In this work, we bridge these gaps by studying the problem of Fair Domain Generalization (FairDG), which ai…

The Illusion of Fairness: Auditing Fairness Interventions with Audit Studies
Disa Sariola, Patrick Button, Aron Culotta, Nicholas Mattei
https://arxiv.org/abs/2507.02152

The Illusion of Fairness: Auditing Fairness Interventions with Audit Studies
Artificial intelligence systems, especially those using machine learning, are being deployed in domains from hiring to loan issuance in order to automate these complex decisions. Judging both the effectiveness and fairness of these AI systems, and their human decision making counterpart, is a complex and important topic studied across both computational and social sciences. Within machine learning, a common way to address bias in downstream classifiers is to resample the training data to offset…

BiFair: A Fairness-aware Training Framework for LLM-enhanced Recommender Systems via Bi-level Optimization
Jiaming Zhang, Yuyuan Li, Yiqun Xu, Li Zhang, Xiaohua Feng, Zhifei Ren, Chaochao Chen
https://arxiv.org/abs/2507.04294

BiFair: 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, the integration of LLMs raises severe fairness concerns. Existing studies reveal that LLM-based RSs exhibit greater unfairness than traditional RSs, yet fairness issues in LLM-enhanced RSs remain largely unexplored. In this paper, our empirical study reveals that while LLM-enh…

Meta-Fair: AI-Assisted Fairness Testing of Large Language Models
Miguel Romero-Arjona, Jos\'e A. Parejo, Juan C. Alonso, Ana B. S\'anchez, Aitor Arrieta, Sergio Segura
https://arxiv.org/abs/2507.02533

Meta-Fair: AI-Assisted Fairness Testing of Large Language Models
Fairness--the absence of unjustified bias--is a core principle in the development of Artificial Intelligence (AI) systems, yet it remains difficult to assess and enforce. Current approaches to fairness testing in large language models (LLMs) often rely on manual evaluation, fixed templates, deterministic heuristics, and curated datasets, making them resource-intensive and difficult to scale. This work aims to lay the groundwork for a novel, automated method for testing fairness in LLMs, reducin…

Fairness-Aware Static and Dynamic Assortment Optimization: Optimal Selection with Balanced Market Share
Omar El Housni, Qing Feng, Huseyin Topaloglu
https://arxiv.org/abs/2507.05606

Fairness-Aware Static and Dynamic Assortment Optimization: Optimal Selection with Balanced Market Share
Assortment optimization is a critical tool for online retailers aiming to maximize revenue. However, optimizing purely for revenue can lead to imbalanced sales across products, potentially causing supplier disengagement and reduced product diversity. To address these fairness concerns, we introduce a market share balancing constraint that limits the disparity in expected sales between any two offered products to a factor of a given parameter $α$. We study both static and dynamic assortment opt…

Fairness Dynamics During Training
Krishna Patel, Nivedha Sivakumar, Barry-John Theobald, Luca Zappella, Nicholas Apostoloff
https://arxiv.org/abs/2506.01709

Fairness Dynamics During Training
We investigate fairness dynamics during Large Language Model (LLM) training to enable the diagnoses of biases and mitigations through training interventions like early stopping; we find that biases can emerge suddenly and do not always follow common performance metrics. We introduce two new metrics to evaluate fairness dynamics holistically during model pre-training: Average Rank and Jensen-Shannon Divergence by Parts. These metrics provide insights into the Pythia models' progression of biases…

This https://arxiv.org/abs/2505.10661 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

It's only fair when I think it's fair: How Gender Bias Alignment Undermines Distributive Fairness in Human-AI Collaboration
Human-AI collaboration is increasingly relevant in consequential areas where AI recommendations support human discretion. However, human-AI teams' effectiveness, capability, and fairness highly depend on human perceptions of AI. Positive fairness perceptions have been shown to foster trust and acceptance of AI recommendations. Yet, work on confirmation bias highlights that humans selectively adhere to AI recommendations that align with their expectations and beliefs -- despite not being necessa…

Toward Valid Measurement Of (Un)fairness For Generative AI: A Proposal For Systematization Through The Lens Of Fair Equality of Chances
Kimberly Le Truong, Annette Zimmermann, Hoda Heidari
https://arxiv.org/abs/2507.04641

Toward Valid Measurement Of (Un)fairness For Generative AI: A Proposal For Systematization Through The Lens Of Fair Equality of Chances
Disparities in the societal harms and impacts of Generative AI (GenAI) systems highlight the critical need for effective unfairness measurement approaches. While numerous benchmarks exist, designing valid measurements requires proper systematization of the unfairness construct. Yet this process is often neglected, resulting in metrics that may mischaracterize unfairness by overlooking contextual nuances, thereby compromising the validity of the resulting measurements. Building on established (u…

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling
Xiyu Zhao, Qimei Cui, Ziqiang Du, Weicai Li, Xi Yu, Wei Ni, Ji Zhang, Xiaofeng Tao, Ping Zhang
https://arxiv.org/abs/2506.02422

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling
Personalized federated learning (PFL) offers a solution to balancing personalization and generalization by conducting federated learning (FL) to guide personalized learning (PL). Little attention has been given to wireless PFL (WPFL), where privacy concerns arise. Performance fairness of PL models is another challenge resulting from communication bottlenecks in WPFL. This paper exploits quantization errors to enhance the privacy of WPFL and proposes a novel quantization-assisted Gaussian differ…

Optimal Adjustment Sets for Nonparametric Estimation of Weighted Controlled Direct Effect
Ruiyang Lin (University of Science,Technology of China), Yongyi Guo (University of Wisconsin-Madison), Kyra Gan (Cornell Tech)
https://arxiv.org/abs/2506.09871

Optimal Adjustment Sets for Nonparametric Estimation of Weighted Controlled Direct Effect
The weighted controlled direct effect (WCDE) generalizes the standard controlled direct effect (CDE) by averaging over the mediator distribution, providing a robust estimate when treatment effects vary across mediator levels. This makes the WCDE especially relevant in fairness analysis, where it isolates the direct effect of an exposure on an outcome, independent of mediating pathways. This work establishes three fundamental advances for WCDE in observational studies: First, we establish necess…

Recovering Fairness Directly from Modularity: a New Way for Fair Community Partitioning
Yufeng Wang, Yiguang Bai, Tianqing Zhu, Ismail Ben Ayed, Jing Yuan
https://arxiv.org/abs/2505.22684

Recovering Fairness Directly from Modularity: a New Way for Fair Community Partitioning
Community partitioning is crucial in network analysis, with modularity optimization being the prevailing technique. However, traditional modularity-based methods often overlook fairness, a critical aspect in real-world applications. To address this, we introduce protected group networks and propose a novel fairness-modularity metric. This metric extends traditional modularity by explicitly incorporating fairness, and we prove that minimizing it yields naturally fair partitions for protected gro…

FairASR: Fair Audio Contrastive Learning for Automatic Speech Recognition
Jongsuk Kim, Jaemyung Yu, Minchan Kwon, Junmo Kim
https://arxiv.org/abs/2506.10747

FairASR: Fair Audio Contrastive Learning for Automatic Speech Recognition
Large-scale ASR models have achieved remarkable gains in accuracy and robustness. However, fairness issues remain largely unaddressed despite their critical importance in real-world applications. In this work, we introduce FairASR, a system that mitigates demographic bias by learning representations that are uninformative about group membership, enabling fair generalization across demographic groups. Leveraging a multi-demographic dataset, our approach employs a gradient reversal layer to suppr…

FairHuman: Boosting Hand and Face Quality in Human Image Generation with Minimum Potential Delay Fairness in Diffusion Models
Yuxuan Wang, Tianwei Cao, Huayu Zhang, Zhongjiang He, Kongming Liang, Zhanyu Ma
https://arxiv.org/abs/2507.02714

FairHuman: Boosting Hand and Face Quality in Human Image Generation with Minimum Potential Delay Fairness in Diffusion Models
Image generation has achieved remarkable progress with the development of large-scale text-to-image models, especially diffusion-based models. However, generating human images with plausible details, such as faces or hands, remains challenging due to insufficient supervision of local regions during training. To address this issue, we propose FairHuman, a multi-objective fine-tuning approach designed to enhance both global and local generation quality fairly. Specifically, we first construct thr…

fairmetrics: An R package for group fairness evaluation
Benjamin Smith, Jianhui Gao, Jessica Gronsbell
https://arxiv.org/abs/2506.06243 https://

fairmetrics: An R package for group fairness evaluation
Fairness is a growing area of machine learning (ML) that focuses on ensuring models do not produce systematically biased outcomes for specific groups, particularly those defined by protected attributes such as race, gender, or age. Evaluating fairness is a critical aspect of ML model development, as biased models can perpetuate structural inequalities. The {fairmetrics} R package offers a user-friendly framework for rigorously evaluating numerous group-based fairness criteria, including metrics…

Learning-Based Multiuser Scheduling in MIMO-OFDM Systems with Hybrid Beamforming
Pouya Agheli, Tugce Kobal, Fran\c{c}ois Durand, Matthew Andrews
https://arxiv.org/abs/2506.08263

Learning-Based Multiuser Scheduling in MIMO-OFDM Systems with Hybrid Beamforming
We investigate the multiuser scheduling problem in multiple-input multiple-output (MIMO) systems using orthogonal frequency division multiplexing (OFDM) and hybrid beamforming in which a base station (BS) communicates with multiple users over millimeter wave (mmWave) channels in the downlink. Improved scheduling is critical for enhancing spectral efficiency and the long-term performance of the system from the perspective of proportional fairness (PF) metric in hybrid beamforming systems due to …

GenFair: Systematic Test Generation for Fairness Fault Detection in Large Language Models
Madhusudan Srinivasan, Jubril Abdel
https://arxiv.org/abs/2506.03024

GenFair: Systematic Test Generation for Fairness Fault Detection in Large Language Models
Large Language Models (LLMs) are increasingly deployed in critical domains, yet they often exhibit biases inherited from training data, leading to fairness concerns. This work focuses on the problem of effectively detecting fairness violations, especially intersectional biases that are often missed by existing template-based and grammar-based testing methods. Previous approaches, such as CheckList and ASTRAEA, provide structured or grammar-driven test generation but struggle with low test diver…

Replaced article(s) found for cs.GT. https://arxiv.org/list/cs.GT/new/
[1/1]:
Beyond Worst-Case Online Allocation via Dynamic Max-min Fairness
https…

Quantifying Query Fairness Under Unawareness
Thomas Jaenich, Alejandro Moreo, Alessandro Fabris, Graham McDonald, Andrea Esuli, Iadh Ounis, Fabrizio Sebastiani
https://arxiv.org/abs/2506.04140

Quantifying Query Fairness Under Unawareness
Traditional ranking algorithms are designed to retrieve the most relevant items for a user's query, but they often inherit biases from data that can unfairly disadvantage vulnerable groups. Fairness in information access systems (IAS) is typically assessed by comparing the distribution of groups in a ranking to a target distribution, such as the overall group distribution in the dataset. These fairness metrics depend on knowing the true group labels for each item. However, when groups are defin…

Replaced article(s) found for cs.CC. https://arxiv.org/list/cs.CC/new
[1/1]:
- From Pseudorandomness to Multi-Group Fairness and Back
Cynthia Dwork, Daniel Lee, Huijia Lin, Pranay Tankala

Monitoring Robustness and Individual Fairness
Ashutosh Gupta, Thomas A. Henzinger, Konstantin Kueffner, Kaushik Mallik, David Pape
https://arxiv.org/abs/2506.00496

Monitoring Robustness and Individual Fairness
Input-output robustness appears in various different forms in the literature, such as robustness of AI models to adversarial or semantic perturbations and individual fairness of AI models that make decisions about humans. We propose runtime monitoring of input-output robustness of deployed, black-box AI models, where the goal is to design monitors that would observe one long execution sequence of the model, and would raise an alarm whenever it is detected that two similar inputs from the past…

I am always amused by the fact that it is common to refer to Fox, WSJ, any NYPost as”Rupert Murdoch’s” but never “A.G. Sulzberger’s New York Times” despite his direct operational control & the obvious editorial stance as champion of the NY aristocracy & punching hippies. https://mastodon.social/@dangil…

This https://arxiv.org/abs/2505.22984 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

A Computational Approach to Improving Fairness in K-means Clustering
The popular K-means clustering algorithm potentially suffers from a major weakness for further analysis or interpretation. Some cluster may have disproportionately more (or fewer) points from one of the subpopulations in terms of some sensitive variable, e.g., gender or race. Such a fairness issue may cause bias and unexpected social consequences. This work attempts to improve the fairness of K-means clustering with a two-stage optimization formulation--clustering first and then adjust cluster …

The American people are driven by fairness and decency (Norman Ornstein/The Contrarian)
https://contrarian.substack.com/p/the-american-people-are-driven-by
http://www.memeorandum.com/250702/p92#a250702p92

This https://arxiv.org/abs/2505.13469 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Algorithmic Tradeoffs in Fair Lending: Profitability, Compliance, and Long-Term Impact
As financial institutions increasingly rely on machine learning models to automate lending decisions, concerns about algorithmic fairness have risen. This paper explores the tradeoff between enforcing fairness constraints (such as demographic parity or equal opportunity) and maximizing lender profitability. Through simulations on synthetic data that reflects real-world lending patterns, we quantify how different fairness interventions impact profit margins and default rates. Our results demonst…

Die ePetition 179771 an den Bundestag aus dem Bereich Gesetzliche Krankenversicherung mit dem Titel
Wechselmöglichkeiten von der PKV in die GKV
kann bis zum Dienstag 15. Juli 2025 01:00 UTC mitgezeichnet werden und lautet
Mit der Petition wird kritisiert, dass zahlreiche ”Kreative Wege” von der PKV zurück zur GKV zu wechseln, existieren.
und wird begründet
Im Sinne der fairness sollten es keinen Weg zurück in die gesetzliche Krankenversicherung geben.
Durc…

The Fair Periodic Assignment Problem
Rolf van Lieshout, Bart van Rossum
https://arxiv.org/abs/2507.04537 https://arxiv.org/pdf/2507.0…

The Fair Periodic Assignment Problem
We study the periodic assignment problem, in which a set of periodically repeating tasks must be assigned to workers within a repeating schedule. The classical efficiency objective is to minimize the number of workers required to operate the schedule. We propose a O(n log n) algorithm to solve this problem. Next, we formalize a notion of fairness among workers, and impose that each worker performs the same work over time. We analyze the resulting trade-off between efficiency and fairness, showi…

Adaptive determinantal scheduling with fairness in wireless networks
H. P. Keeler, B. B{\l}aszczyszyn
https://arxiv.org/abs/2506.11738 https://

Adaptive determinantal scheduling with fairness in wireless networks
We propose a novel framework for wireless network scheduling with fairness using determinantal (point) processes. Our approach incorporates the repulsive nature of determinantal processes, generalizing traditional Aloha protocols that schedule transmissions independently. We formulate the scheduling problem with an utility function representing fairness. We then recast this formulation as a convex optimization problem over a certain class of determinantal point processes called $L$-ensembles, w…

Gender Inclusivity Fairness Index (GIFI): A Multilevel Framework for Evaluating Gender Diversity in Large Language Models
Zhengyang Shan, Emily Ruth Diana, Jiawei Zhou
https://arxiv.org/abs/2506.15568

Gender Inclusivity Fairness Index (GIFI): A Multilevel Framework for Evaluating Gender Diversity in Large Language Models
We present a comprehensive evaluation of gender fairness in large language models (LLMs), focusing on their ability to handle both binary and non-binary genders. While previous studies primarily focus on binary gender distinctions, we introduce the Gender Inclusivity Fairness Index (GIFI), a novel and comprehensive metric that quantifies the diverse gender inclusivity of LLMs. GIFI consists of a wide range of evaluations at different levels, from simply probing the model with respect to provide…

My article just out:
Progressive Jews (and non-Jews too!) need to revive the social democratic politics of solidarity. It brings poor & middle together, in contrast to the politics of far-left "allyship" & elite charity which both drive them apart.
https://www.

After Zohran Mamdani’s upset, there’s a way forward for pro-Israel progressives
In the fight for economic fairness for all, Jews shouldn't have to give up on defending their particular interests, writes a long-time contributor to Dissent magazine.

BOOST: Out-of-Distribution-Informed Adaptive Sampling for Bias Mitigation in Stylistic Convolutional Neural Networks
Mridula Vijendran, Shuang Chen, Jingjing Deng, Hubert P. H. Shum
https://arxiv.org/abs/2507.07134

BOOST: Out-of-Distribution-Informed Adaptive Sampling for Bias Mitigation in Stylistic Convolutional Neural Networks
The pervasive issue of bias in AI presents a significant challenge to painting classification, and is getting more serious as these systems become increasingly integrated into tasks like art curation and restoration. Biases, often arising from imbalanced datasets where certain artistic styles dominate, compromise the fairness and accuracy of model predictions, i.e., classifiers are less accurate on rarely seen paintings. While prior research has made strides in improving classification performa…

This https://arxiv.org/abs/2504.02856 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

The epistemic dimension of algorithmic fairness: assessing its impact in innovation diffusion and fair policy making
Algorithmic fairness is an expanding field that addresses a range of discrimination issues associated with algorithmic processes. However, most works in the literature focus on analyzing it only from an ethical perspective, focusing on moral principles and values that should be considered in the design and evaluation of algorithms, while disregarding the epistemic dimension related to knowledge transmission and validation. However, this aspect of algorithmic fairness should also be included in …

Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System
Yongsen Zheng, Zongxuan Xie, Guohua Wang, Ziyao Liu, Liang Lin, Kwok-Yan Lam
https://arxiv.org/abs/2507.02000

Why 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 approaches have started to improve fairness recommendation in offline or static contexts, the issue of unfairness often exacerbates over time, leading to significant problems like the Matthew effect, filter bubbles, and echo chambers. To address these challenges, we proposed a novel fram…

This https://arxiv.org/abs/2401.17909 has been replaced.
link: https://scholar.google.com/scholar?q=a

Regularizing Fairness in Optimal Policy Learning with Distributional Targets
A decision maker typically (i) incorporates training data to learn about the relative effectiveness of treatments, and (ii) chooses an implementation mechanism that implies an ``optimal'' predicted outcome distribution according to some target functional. Nevertheless, a fairness-aware decision maker may not be satisfied achieving said optimality at the cost of being ``unfair" against a subgroup of the population, in the sense that the outcome distribution in that subgroup deviates too strongly…

On the Language and Gender Biases in PSTN, VoIP and Neural Audio Codecs
Kemal Altwlkany, Amar Kuric, Emanuel Lacic
https://arxiv.org/abs/2506.02545 https:/…

On the Language and Gender Biases in PSTN, VoIP and Neural Audio Codecs
In recent years, there has been a growing focus on fairness and inclusivity within speech technology, particularly in areas such as automatic speech recognition and speech sentiment analysis. When audio is transcoded prior to processing, as is the case in streaming or real-time applications, any inherent bias in the coding mechanism may result in disparities. This not only affects user experience but can also have broader societal implications by perpetuating stereotypes and exclusion. Thus, it…

The Rich Get Richer in Bitcoin Mining Induced by Blockchain Forks
Akira Sakurai, Kazuyuki Shudo
https://arxiv.org/abs/2506.13360 https://

The Rich Get Richer in Bitcoin Mining Induced by Blockchain Forks
Bitcoin is a representative decentralized currency system. For the security of Bitcoin, fairness in the distribution of mining rewards plays a crucial role in preventing the concentration of computational power in a few miners. Here, fairness refers to the distribution of block rewards in proportion to contributed computational resources. If miners with greater computational resources receive disproportionately higher rewards, i.e., if the Rich Get Richer (TRGR) phenomenon holds in Bitcoin, it …

Meta Optimality for Demographic Parity Constrained Regression via Post-Processing
Kazuto Fukuchi
https://arxiv.org/abs/2506.13947 https://

Meta Optimality for Demographic Parity Constrained Regression via Post-Processing
We address the regression problem under the constraint of demographic parity, a commonly used fairness definition. Recent studies have revealed fair minimax optimal regression algorithms, the most accurate algorithms that adhere to the fairness constraint. However, these analyses are tightly coupled with specific data generation models. In this paper, we provide meta-theorems that can be applied to various situations to validate the fair minimax optimality of the corresponding regression algori…

Fair and Efficient Allocation of Indivisible Mixed Manna
Siddharth Barman, Vishwa Prakash HV, Aditi Sethia, Mashbat Suzuki
https://arxiv.org/abs/2507.03946

Fair and Efficient Allocation of Indivisible Mixed Manna
We study fair division of indivisible mixed manna (items whose values may be positive, negative, or zero) among agents with additive valuations. Here, we establish that fairness -- in terms of a relaxation of envy-freeness -- and Pareto efficiency can always be achieved together. Specifically, our fairness guarantees are in terms of envy-freeness up to $k$ reallocations (EFR-$k$): An allocation $A$ of the indivisible items is said to be EFR-$k$ if there exists a subset $R$ of at most $k$ items …

Fair Deepfake Detectors Can Generalize
Harry Cheng, Ming-Hui Liu, Yangyang Guo, Tianyi Wang, Liqiang Nie, Mohan Kankanhalli
https://arxiv.org/abs/2507.02645

Fair Deepfake Detectors Can Generalize
Deepfake detection models face two critical challenges: generalization to unseen manipulations and demographic fairness among population groups. However, existing approaches often demonstrate that these two objectives are inherently conflicting, revealing a trade-off between them. In this paper, we, for the first time, uncover and formally define a causal relationship between fairness and generalization. Building on the back-door adjustment, we show that controlling for confounders (data distri…

Minority Representation in Network Rankings: Methods for Estimation, Testing, and Fairness
Hui Shen, Peter W. MacDonald, Eric D. Kolaczyk
https://arxiv.org/abs/2507.01136

Minority Representation in Network Rankings: Methods for Estimation, Testing, and Fairness
Networks, composed of nodes and their connections, are widely used to model complex relationships across various fields. Centrality metrics often inform decisions such as identifying key nodes or prioritizing resources. However, networks frequently suffer from missing or incorrect edges, which can systematically centrality-based decisions and distort the representation of certain protected groups. To address this issue, we introduce a formal definition of minority representation, measured as th…

An Efficient Max-Min Fair Resource Optimization Algorithm for Rate-Splitting Multiple Access
Facheng Luo, Yijie Mao
https://arxiv.org/abs/2507.04201 https:…

An Efficient Max-Min Fair Resource Optimization Algorithm for Rate-Splitting Multiple Access
The max-min fairness (MMF) problem in rate-splitting multiple access (RSMA) is known to be challenging due to its non-convex and non-smooth nature, as well as the coupled beamforming and common rate variables. Conventional algorithms to address this problem often incur high computational complexity or degraded MMF rate performance. To address these challenges, in this work, we propose a novel optimization algorithm named extragradient-fractional programming (EG-FP) to address the MMF problem of…

Software Fairness Testing in Practice
Ronnie de Souza Santos, Matheus de Morais Leca, Reydne Santos, Cleyton Magalhaes
https://arxiv.org/abs/2506.17095 htt…

Software Fairness Testing in Practice
Software testing ensures that a system functions correctly, meets specified requirements, and maintains high quality. As artificial intelligence and machine learning (ML) technologies become integral to software systems, testing has evolved to address their unique complexities. A critical advancement in this space is fairness testing, which identifies and mitigates biases in AI applications to promote ethical and equitable outcomes. Despite extensive academic research on fairness testing, inclu…

I think we are all astonished to see Agyemang: 1) stay onside; 2) delicately play around the keeper; 3) put a tight angle shot on target.
He’s shown promising physicality, but little else. I’ve long ago decided he’s a siren on the rocks.
In fairness, nice goal here though.
#GoldCup #USMNT…

This https://arxiv.org/abs/2505.24171 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

A note on the diversity Owen values
Béal et al. (Int J Game Theory 54, 2025) introduce the Diversity Owen value for TU-games with diversity constraints, and provide axiomatic characterizations using the axioms of fairness and balanced contributions. However, there exist logical flaws in the proofs of the uniqueness of these characterizations. In this note we provides the corrected proofs of the characterizations by introducing the null player for diversity games axiom. Also, we establish two alternative characterizations of the …

Balancing Profit and Fairness in Risk-Based Pricing Markets
Jesse Thibodeau, Hadi Nekoei, Afaf Ta\"ik, Janarthanan Rajendran, Golnoosh Farnadi
https://arxiv.org/abs/2506.00140

Balancing Profit and Fairness in Risk-Based Pricing Markets
Dynamic, risk-based pricing can systematically exclude vulnerable consumer groups from essential resources such as health insurance and consumer credit. We show that a regulator can realign private incentives with social objectives through a learned, interpretable tax schedule. First, we provide a formal proposition that bounding each firm's \emph{local} demographic gap implicitly bounds the \emph{global} opt-out disparity, motivating firm-level penalties. Building on this insight we introduce …

Beyond the Black Box: Interpretability of LLMs in Finance
Hariom Tatsat (Barclays), Ariye Shater (Barclays)
https://arxiv.org/abs/2505.24650 https://

Beyond the Black Box: Interpretability of LLMs in Finance
Large Language Models (LLMs) exhibit remarkable capabilities across a spectrum of tasks in financial services, including report generation, chatbots, sentiment analysis, regulatory compliance, investment advisory, financial knowledge retrieval, and summarization. However, their intrinsic complexity and lack of transparency pose significant challenges, especially in the highly regulated financial sector, where interpretability, fairness, and accountability are critical. As far as we are aware, t…

This https://arxiv.org/abs/2504.05307 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Toward Total Recall: Enhancing FAIRness through AI-Driven Metadata Standardization
Scientific metadata often suffer from incompleteness, inconsistency, and formatting errors, which hinder effective discovery and reuse of the associated datasets. We present a method that combines GPT-4 with structured metadata templates from the CEDAR knowledge base to automatically standardize metadata and to ensure compliance with established standards. A CEDAR template specifies the expected fields of a metadata submission and their permissible values. Our standardization process involves u…

Replaced article(s) found for stat.AP. https://arxiv.org/list/stat.AP/new
[1/1]:
- Fairness is in the details : Face Dataset Auditing
Valentin Lafargue, Emmanuelle Claeys, Jean-Michel Loubes

Replaced article(s) found for cs.GT. https://arxiv.org/list/cs.GT/new/
[1/1]:
Epistemic vs. Counterfactual Fairness in Allocation of Resources
https…

Evaluating the Efficacy of LLM-Based Reasoning for Multiobjective HPC Job Scheduling
Prachi Jadhav, Hongwei Jin, Ewa Deelman, Prasanna Balaprakash
https://arxiv.org/abs/2506.02025

Evaluating the Efficacy of LLM-Based Reasoning for Multiobjective HPC Job Scheduling
High-Performance Computing (HPC) job scheduling involves balancing conflicting objectives such as minimizing makespan, reducing wait times, optimizing resource use, and ensuring fairness. Traditional methods, including heuristic-based (e.g., First-Come-First-Served) or intensive optimization techniques, often lack adaptability to dynamic workloads and heterogeneous HPC systems. To address this, we propose a novel Large Language Model (LLM)-based scheduler using a ReAct-style framework (Reason +…

Multimodal Visual Image Based User Association and Beamforming Using Graph Neural Networks
Yinghan Li, Yiming Liu, Wei Yu
https://arxiv.org/abs/2506.18218 …

Multimodal Visual Image Based User Association and Beamforming Using Graph Neural Networks
This paper proposes an approach that leverages multimodal data by integrating visual images with radio frequency (RF) pilots to optimize user association and beamforming in a downlink wireless cellular network under a max-min fairness criterion. Traditional methods typically optimize wireless system parameters based on channel state information (CSI). However, obtaining accurate CSI requires extensive pilot transmissions, which lead to increased overhead and latency. Moreover, the optimization …

The Rich Get Richer in Bitcoin Mining Induced by Blockchain Forks
Akira Sakurai, Kazuyuki Shudo
https://arxiv.org/abs/2506.13360 https://

The Rich Get Richer in Bitcoin Mining Induced by Blockchain Forks
Bitcoin is a representative decentralized currency system. For the security of Bitcoin, fairness in the distribution of mining rewards plays a crucial role in preventing the concentration of computational power in a few miners. Here, fairness refers to the distribution of block rewards in proportion to contributed computational resources. If miners with greater computational resources receive disproportionately higher rewards, i.e., if the Rich Get Richer (TRGR) phenomenon holds in Bitcoin, it …

This https://arxiv.org/abs/2310.03475 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGT_…

Fair Division with Allocator's Preference
We study the fair allocation of indivisible resources among agents. Most prior work focuses on fairness and/or efficiency among agents. However, the allocator, as the resource owner, may also be involved in many scenarios (e.g., government resource allocation, heritage division, company personnel assignment, etc). The allocator inclines to obtain a fair or efficient allocation based on her preference over the items and to whom each item is allocated. We propose a model and study two problems: 1…

Low-Perplexity LLM-Generated Sequences and Where To Find Them
Arthur Wuhrmann, Anastasiia Kucherenko, Andrei Kucharavy
https://arxiv.org/abs/2507.01844 htt…

Low-Perplexity LLM-Generated Sequences and Where To Find Them
As Large Language Models (LLMs) become increasingly widespread, understanding how specific training data shapes their outputs is crucial for transparency, accountability, privacy, and fairness. To explore how LLMs leverage and replicate their training data, we introduce a systematic approach centered on analyzing low-perplexity sequences - high-probability text spans generated by the model. Our pipeline reliably extracts such long sequences across diverse topics while avoiding degeneration, the…

Ethics by Design: A Lifecycle Framework for Trustworthy AI in Medical Imaging From Transparent Data Governance to Clinically Validated Deployment
Umer Sadiq Khan, Saif Ur Rehman Khan
https://arxiv.org/abs/2507.04249

Ethics by Design: A Lifecycle Framework for Trustworthy AI in Medical Imaging From Transparent Data Governance to Clinically Validated Deployment
The integration of artificial intelligence (AI) in medical imaging raises crucial ethical concerns at every stage of its development, from data collection to deployment. Addressing these concerns is essential for ensuring that AI systems are developed and implemented in a manner that respects patient rights and promotes fairness. This study aims to explore the ethical implications of AI in medical imaging, focusing on five key stages: data collection, data processing, model training, model eval…

This https://arxiv.org/abs/2504.05678 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

On The Merit Principle in Strategic Exchange
New fairness notions in align with the merit principle are proposed for designing exchange rules. We show that, for an obviously strategy-proof, efficient and individually rational rule, an upper bound of fairness attainable is that, if two agents possess objects considered the best by all others divided into two equal groups, then at least one receives her favorite object. Notably, it is not possible to guarantee them both receiving favorites. Our results thus indicate an unambiguous trade-off…

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis
Zuzanna Skorniewska, Bartlomiej W. Papiez
https://arxiv.org/abs/2506.11753

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis
The adoption of neural network models in medical imaging has been constrained by strict privacy regulations, limited data availability, high acquisition costs, and demographic biases. Deep generative models offer a promising solution by generating synthetic data that bypasses privacy concerns and addresses fairness by producing samples for under-represented groups. However, unlike natural images, medical imaging requires validation not only for fidelity (e.g., Fréchet Inception Score) but also…

WavShape: Information-Theoretic Speech Representation Learning for Fair and Privacy-Aware Audio Processing
Oguzhan Baser, Ahmet Ege Tanriverdi, Kaan Kale, Sandeep P. Chinchali, Sriram Vishwanath
https://arxiv.org/abs/2506.22789

WavShape: Information-Theoretic Speech Representation Learning for Fair and Privacy-Aware Audio Processing
Speech embeddings often retain sensitive attributes such as speaker identity, accent, or demographic information, posing risks in biased model training and privacy leakage. We propose WavShape, an information-theoretic speech representation learning framework that optimizes embeddings for fairness and privacy while preserving task-relevant information. We leverage mutual information (MI) estimation using the Donsker-Varadhan formulation to guide an MI-based encoder that systematically filters s…

This https://arxiv.org/abs/2506.00140 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Balancing Profit and Fairness in Risk-Based Pricing Markets
Dynamic, risk-based pricing can systematically exclude vulnerable consumer groups from essential resources such as health insurance and consumer credit. We show that a regulator can realign private incentives with social objectives through a learned, interpretable tax schedule. First, we provide a formal proposition that bounding each firm's \emph{local} demographic gap implicitly bounds the \emph{global} opt-out disparity, motivating firm-level penalties. Building on this insight we introduce …

This https://arxiv.org/abs/2505.11396 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Finding Counterfactual Evidences for Node Classification
Counterfactual learning is emerging as an important paradigm, rooted in causality, which promises to alleviate common issues of graph neural networks (GNNs), such as fairness and interpretability. However, as in many real-world application domains where conducting randomized controlled trials is impractical, one has to rely on available observational (factual) data to detect counterfactuals. In this paper, we introduce and tackle the problem of searching for counterfactual evidences for the GNN…

This https://arxiv.org/abs/2308.15651 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Ensuring User-side Fairness in Dynamic Recommender Systems
User-side group fairness is crucial for modern recommender systems, aiming to alleviate performance disparities among user groups defined by sensitive attributes like gender, race, or age. In the ever-evolving landscape of user-item interactions, continual adaptation to newly collected data is crucial for recommender systems to stay aligned with the latest user preferences. However, we observe that such continual adaptation often exacerbates performance disparities. This necessitates a thorough…

Online Fair Division with Additional Information
Tzeh Yuan Neoh, Jannik Peters, Nicholas Teh
https://arxiv.org/abs/2505.24503 https://

Online Fair Division with Additional Information
We study the problem of fairly allocating indivisible goods to agents in an online setting, where goods arrive sequentially and must be allocated irrevocably to agents. Focusing on the popular fairness notions of envy-freeness, proportionality, and maximin share fairness (and their approximate variants), we ask how the availability of information on future goods influences the existence and approximability of fair allocations. In the absence of any such information, we establish strong impossib…

Understanding and Benchmarking the Trustworthiness in Multimodal LLMs for Video Understanding
Youze Wang, Zijun Chen, Ruoyu Chen, Shishen Gu, Yinpeng Dong, Hang Su, Jun Zhu, Meng Wang, Richang Hong, Wenbo Hu
https://arxiv.org/abs/2506.12336

Understanding and Benchmarking the Trustworthiness in Multimodal LLMs for Video Understanding
Recent advancements in multimodal large language models for video understanding (videoLLMs) have improved their ability to process dynamic multimodal data. However, trustworthiness challenges factual inaccuracies, harmful content, biases, hallucinations, and privacy risks, undermine reliability due to video data's spatiotemporal complexities. This study introduces Trust-videoLLMs, a comprehensive benchmark evaluating videoLLMs across five dimensions: truthfulness, safety, robustness, fairness, …

Fair Bayesian Model-Based Clustering
Jihu Lee, Kunwoong Kim, Yongdai Kim
https://arxiv.org/abs/2506.12839 https://arxiv.org/pdf/2506.…

Fair Bayesian Model-Based Clustering
Fair clustering has become a socially significant task with the advancement of machine learning technologies and the growing demand for trustworthy AI. Group fairness ensures that the proportions of each sensitive group are similar in all clusters. Most existing group-fair clustering methods are based on the $K$-means clustering and thus require the distance between instances and the number of clusters to be given in advance. To resolve this limitation, we propose a fair Bayesian model-based cl…

Evaluating Scoring Bias in LLM-as-a-Judge
Qingquan Li, Shaoyu Dou, Kailai Shao, Chao Chen, Haixiang Hu
https://arxiv.org/abs/2506.22316 https://

Evaluating Scoring Bias in LLM-as-a-Judge
The remarkable performance of Large Language Models (LLMs) gives rise to``LLM-as-a-Judge'', where LLMs are employed as evaluators for complex tasks. Moreover, it has been widely adopted across fields such as Natural Language Processing (NLP), preference learning, and various specific domains. However, there are various biases within LLM-as-a-Judge, which adversely affect the fairness and reliability of judgments. Current research on evaluating or mitigating bias in LLM-as-a-Judge predominantly …

This https://arxiv.org/abs/2505.14449 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach
While subgroup disparities and performance bias are increasingly studied in computational research, fairness in categorical Speech Emotion Recognition (SER) remains underexplored. Existing methods often rely on explicit demographic labels, which are difficult to obtain due to privacy concerns. To address this limitation, we introduce an Implicit Demography Inference (IDI) module that leverages pseudo-labeling from a pre-trained model and unsupervised learning using k-means clustering to mitigat…

Visibly Fair Mechanisms
In\'acio B\'o, Gian Caspari, Manshu Khanna
https://arxiv.org/abs/2506.19176 https://arxiv.org/pdf/250…

Visibly Fair Mechanisms
Priority-based allocation of individuals to positions are pervasive, and elimination of justified envy is often, an absolute requirement. This leaves serial dictatorship (SD) as the only rule that avoids justified envy under standard direct mechanisms. What if SD outcomes are undesirable from a designer's perspective? We propose visible fairness, which demands fairness relative to the (potentially purposefully incomplete) preference information the mechanism elicits. Visibly fair mechanisms gen…

This https://arxiv.org/abs/2503.05728 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Political Neutrality in AI Is Impossible- But Here Is How to Approximate It
AI systems often exhibit political bias, influencing users' opinions and decisions. While political neutrality-defined as the absence of bias-is often seen as an ideal solution for fairness and safety, this position paper argues that true political neutrality is neither feasible nor universally desirable due to its subjective nature and the biases inherent in AI training data, algorithms, and user interactions. However, inspired by Joseph Raz's philosophical insight that "neutrality [...] can b…

Online Fair Allocations with Binary Valuations and Beyond
Yuanyuan Wang, Tianze Wei
https://arxiv.org/abs/2505.24321 https://arxiv.or…

Online Fair Allocations with Binary Valuations and Beyond
In an online fair allocation problem, a sequence of indivisible items arrives online and needs to be allocated to offline agents immediately and irrevocably. In our paper, we study the online allocation of either goods or chores. We adapt popular fairness notions, including envy-freeness up to one item (EF1) and maximin share fairness (MMS), and use utilitarian social welfare (USW) to measure efficiency. For both settings of items, we present a series of positive results regarding the existence…

benefits, taxes, Starmer etc
Well done disability activists for championing (relative) fairness, care and sense. You shouldn't have had to deal with that cruel nonsense, but you led the way, and I appreciate it.
Tip of the hat to Labour rebels.
Keir Starmer you should fix the tax system now. #TaxWealthNotWork.
There's a campaign for a new 2% tax on wealth over £10,000,000, which seems entirely reasonable to me.
#disability #benefits #UK #UKPol #KeirStarmer #LabourParty

Achieving Equitability with Subsidy
Yuanyuan Wang, Tianze Wei
https://arxiv.org/abs/2505.23251 https://arxiv.org/pdf/2505.23251

Achieving Equitability with Subsidy
We study the fair allocation problem of indivisible items with subsidies. In this paper, we mainly consider the notion of fairness - equitability (EQ), which requires that items be allocated such that all agents value the bundle they receive equally. Firstly, we study the upper bounds of the required subsidy to achieve EQ in different settings of items and provide the corresponding lower bounds. Secondly, we consider the bounded subsidy for achieving EQ and another popular notion of fairness - …

Replaced article(s) found for econ.EM. https://arxiv.org/list/econ.EM/new
[1/1]:
Inference for an Algorithmic Fairness-Accuracy Frontier
https://…

hyperFA*IR: A hypergeometric approach to fair rankings with finite candidate pool
Mauritz N. Cartier van Dissel, Samuel Martin-Gutierrez, Lisette Esp\'in-Noboa, Ana Mar\'ia Jaramillo, Fariba Karimi
https://arxiv.org/abs/2506.14349

hyperFA*IR: A hypergeometric approach to fair rankings with finite candidate pool
Ranking algorithms play a pivotal role in decision-making processes across diverse domains, from search engines to job applications. When rankings directly impact individuals, ensuring fairness becomes essential, particularly for groups that are marginalised or misrepresented in the data. Most of the existing group fairness frameworks often rely on ensuring proportional representation of protected groups. However, these approaches face limitations in accounting for the stochastic nature of rank…

I'm giving away some technical #books, preferably around #Osona / #Vic , but any #Catalonia town around the R3 line down to #Barcelona is also ok. I don't have a car and I'd prefer not having to package and send them.
The publisher made some mistakes so I have duplicates, the ones I'm giving away:
- Math for Security
- The Shape of Data
- Statistics Done Wrong
- The Rust Programming Language (2021 Edition)
I'd also prefer giving it to people who can't afford buying expensive books often, for economical fairness reasons.
⁨#Giveaway⁩ ⁨#donation⁩ #bookstodon #freebooks

A note on the diversity Owen values
Songtao He, Erfang Shan, Xinyu Sun
https://arxiv.org/abs/2505.24171 https://arxiv.org/pdf/2505.24…

A note on the diversity Owen values
Béal et al. (Int J Game Theory 54, 2025) introduce the Diversity Owen value for TU-games with diversity constraints, and provide axiomatic characterizations using the axioms of fairness and balanced contributions. However, there exist logical flaws in the proofs of the uniqueness of these characterizations. In this note we provides the corrected proofs of the characterizations by introducing the null player for diversity games axiom. Also, we establish two alternative characterizations of the …

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/7]:
- Sublinear Algorithms for Wasserstein and Total Variation Distances: Applications to Fairness and ...
Debabrota Basu, Debarshi Chanda

Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement
Maryam Mousavian, Zahra Abbasiantaeb, Mohammad Aliannejadi, Fabio Crestani
https://arxiv.org/abs/2506.22372

Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement
The presence of social biases in Natural Language Processing (NLP) and Information Retrieval (IR) systems is an ongoing challenge, which underlines the importance of developing robust approaches to identifying and evaluating such biases. In this paper, we aim to address this issue by leveraging Large Language Models (LLMs) to detect and measure gender bias in passage ranking. Existing gender fairness metrics rely on lexical- and frequency-based measures, leading to various limitations, e.g., mi…

This https://arxiv.org/abs/2402.08533 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGT_…

Grace Period is All You Need: Individual Fairness without Revenue Loss in Revenue Management
Imagine you and a friend purchase identical items at a store, yet only your friend received a discount. Would your friend's discount make you feel unfairly treated by the store? And would you be less willing to purchase from that store again in the future? Based on a large-scale online survey that we ran on Prolific, it turns out that the answers to the above questions are positive. Therefore, when allocating resources to different customers, sellers should consider both the total reward and in…

Replaced article(s) found for cs.CY. https://arxiv.org/list/cs.CY/new
[1/1]:
- Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey
Fabris, Baranowska, Dennis, Graus, Hacker, Saldivar, Borgesius, Biega

This https://arxiv.org/abs/2410.23394 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Auditing for Bias in Ad Delivery Using Inferred Demographic Attributes
Auditing social-media algorithms has become a focus of public-interest research and policymaking to ensure their fairness across demographic groups such as race, age, and gender in consequential domains such as the presentation of employment opportunities. However, such demographic attributes are often unavailable to auditors and platforms. When demographics data is unavailable, auditors commonly infer them from other available information. In this work, we study the effects of inference error …

The Disparate Effects of Partial Information in Bayesian Strategic Learning
Srikanth Avasarala, Serena Wang, Juba Ziani
https://arxiv.org/abs/2506.00627 ht…

The Disparate Effects of Partial Information in Bayesian Strategic Learning
We study how partial information about scoring rules affects fairness in strategic learning settings. In strategic learning, a learner deploys a scoring rule, and agents respond strategically by modifying their features -- at some cost -- to improve their outcomes. However, in our work, agents do not observe the scoring rule directly; instead, they receive a noisy signal of said rule. We consider two different agent models: (i) naive agents, who take the noisy signal at face value, and (ii) Bay…

Alleviating User-Sensitive bias with Fair Generative Sequential Recommendation Model
Yang Liu, Feng Wu, Xuefang Zhu
https://arxiv.org/abs/2506.19777 https:…

Alleviating User-Sensitive bias with Fair Generative Sequential Recommendation Model
Recommendation fairness has recently attracted much attention. In the real world, recommendation systems are driven by user behavior, and since users with the same sensitive feature (e.g., gender and age) tend to have the same patterns, recommendation models can easily capture the strong correlation preference of sensitive features and thus cause recommendation unfairness. Diffusion model (DM) as a new generative model paradigm has achieved great success in recommendation systems. DM's ability …

This https://arxiv.org/abs/2205.14296 has been replaced.
link: https://scholar.google.com/scholar?q=a

Approximability Landscape of Welfare Maximization within Fair Allocations
Fair allocation of indivisible goods studies allocating $m$ goods among $n$ agents in a fair manner. While fairness is a fundamental requirement in many real-world applications, it often conflicts with (economic) efficiency. This raises a natural and important question: How can we identify the most welfare-efficient allocation among all fair allocations? This paper answers from the perspective of computational complexity. Specifically, we study the problem of maximizing utilitarian social welfa…

A Transformer-Based Neural Network for Optimal Deterministic-Allocation and Anonymous Joint Auction Design
Zhen Zhang, Luowen Liu, Wanzhi Zhang, Zitian Guo, Kun Huang, Qi Qi, Qiang Liu, Xingxing Wang
https://arxiv.org/abs/2506.02435

A Transformer-Based Neural Network for Optimal Deterministic-Allocation and Anonymous Joint Auction Design
With the advancement of machine learning, an increasing number of studies are employing automated mechanism design (AMD) methods for optimal auction design. However, all previous AMD architectures designed to generate optimal mechanisms that satisfy near dominant strategy incentive compatibility (DSIC) fail to achieve deterministic allocation, and some also lack anonymity, thereby impacting the efficiency and fairness of advertising allocation. This has resulted in a notable discrepancy between…

An Axiomatization of the Random Priority Rule
Christian Basteck
https://arxiv.org/abs/2506.17997 https://arxiv.org/pdf/2506.17997

An Axiomatization of the Random Priority Rule
We study the problem of assigning indivisible objects to agents where each is to receive at most one. To ensure fairness in the absence of monetary compensation, we consider random assignments. Random Priority, also known as Random Serial Dictatorship, is characterized by equal-treatment-of-equals, ex-post efficiency and probabilistic (Maskin) monotonicity -- whenever preferences change so that a given deterministic assignment is ranked weakly higher by all agents, the probability of that assig…

Near-feasible Fair Allocations in Two-sided Markets
Javier Cembrano, Andr\'es Moraga, Victor Verdugo
https://arxiv.org/abs/2506.01178 https://

Near-feasible Fair Allocations in Two-sided Markets
We study resource allocation in two-sided markets from a fundamental perspective and introduce a general modeling and algorithmic framework to effectively incorporate the complex and multidimensional aspects of fairness. Our main technical contribution is to show the existence of a range of near-feasible resource allocations parameterized in different model primitives to give flexibility when balancing the different policymaking requirements, allowing policy designers to fix these values accord…

Can We Trust Machine Learning? The Reliability of Features from Open-Source Speech Analysis Tools for Speech Modeling
Tahiya Chowdhury, Veronica Romero
https://arxiv.org/abs/2506.11072

Can We Trust Machine Learning? The Reliability of Features from Open-Source Speech Analysis Tools for Speech Modeling
Machine learning-based behavioral models rely on features extracted from audio-visual recordings. The recordings are processed using open-source tools to extract speech features for classification models. These tools often lack validation to ensure reliability in capturing behaviorally relevant information. This gap raises concerns about reproducibility and fairness across diverse populations and contexts. Speech processing tools, when used outside of their design context, can fail to capture b…

On Hierarchies of Fairness Notions in Cake Cutting: From Proportionality to Super Envy-Freeness
Arnav Mehra, Alexandros Psomas
https://arxiv.org/abs/2506.12950

On Hierarchies of Fairness Notions in Cake Cutting: From Proportionality to Super Envy-Freeness
We consider the classic cake-cutting problem of producing fair allocations for $n$ agents, in the Robertson-Webb query model. In this model, it is known that: (i) proportional allocations can be computed using $O(n \log n)$ queries, and this is optimal for deterministic protocols; (ii) envy-free allocations (a subset of proportional allocations) can be computed using $O\left( n^{n^{n^{n^{n^{n}}}}} \right)$ queries, and the best known lower bound is $Ω(n^2)$; (iii) perfect allocations (a subset…

On Devon Allen's Disqualification at the 2022 World Track and Field Championships
Owen Fiore, Elizabeth D. Schifano, Jun Yan
https://arxiv.org/abs/2506.11460

On Devon Allen's Disqualification at the 2022 World Track and Field Championships
Devon Allen's disqualification at the men's 110-meter hurdle final at the 2022 World Track and Field Championships, due to a reaction time (RT) of 0.099 seconds-just 0.001 seconds below the allowable threshold-sparked widespread debate over the fairness and validity of RT rules. This study investigates two key issues: variations in timing systems and the justification for the 0.1-second disqualification threshold. We pooled RT data from men's 110-meter hurdles and 100-meter dash, as well as wom…

Simultaneously Fair Allocation of Indivisible Items Across Multiple Dimensions
Yasushi Kawase, Bodhayan Roy, Mohammad Azharuddin Sanpui
https://arxiv.org/abs/2506.21727

Simultaneously Fair Allocation of Indivisible Items Across Multiple Dimensions
This paper explores the fair allocation of indivisible items in a multidimensional setting, motivated by the need to address fairness in complex environments where agents assess bundles according to multiple criteria. Such multidimensional settings are not merely of theoretical interest but are central to many real-world applications. For example, cloud computing resources are evaluated based on multiple criteria such as CPU cores, memory, and network bandwidth. In such cases, traditional one d…

Bias Delayed is Bias Denied? Assessing the Effect of Reporting Delays on Disparity Assessments
Jennah Gosciak (Cornell University), Aparna Balagopalan (Massachusetts Institute of Technology), Derek Ouyang (Stanford University), Allison Koenecke (Cornell University), Marzyeh Ghassemi (Massachusetts Institute of Technology), Daniel E. Ho (Stanford University)

Bias Delayed is Bias Denied? Assessing the Effect of Reporting Delays on Disparity Assessments
Conducting disparity assessments at regular time intervals is critical for surfacing potential biases in decision-making and improving outcomes across demographic groups. Because disparity assessments fundamentally depend on the availability of demographic information, their efficacy is limited by the availability and consistency of available demographic identifiers. While prior work has considered the impact of missing data on fairness, little attention has been paid to the role of delayed dem…

Fair Contracts in Principal-Agent Games with Heterogeneous Types
Jakub T{\l}uczek, Victor Villin, Christos Dimitrakakis
https://arxiv.org/abs/2506.15887 ht…

Fair Contracts in Principal-Agent Games with Heterogeneous Types
Fairness is desirable yet challenging to achieve within multi-agent systems, especially when agents differ in latent traits that affect their abilities. This hidden heterogeneity often leads to unequal distributions of wealth, even when agents operate under the same rules. Motivated by real-world examples, we propose a framework based on repeated principal-agent games, where a principal, who also can be seen as a player of the game, learns to offer adaptive contracts to agents. By leveraging a …