{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/fairness/papers/21","list_of":"/task/fairness","task":"Fairness","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":21,"pages_in_order":57,"rows_per_page":100,"rows":[2001,2100],"of":5676,"counts":{"archive_papers_tagged":5676,"with_a_code_link":1714,"where_syntology_ran_a_sample":404,"not_listed_spam_title":0,"listed":5676,"listed_where_code_ran":404,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":329,"every_run_a_failure_of_syntologys_instrument":75,"listed_with_a_run_with_no_instrument_failure":329,"listed_every_run_a_failure_of_syntologys_instrument":75,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/fairness","prev":"/task/fairness/papers/20","next":"/task/fairness/papers/22","papers":[{"url":null,"slug":"efficient-first-order-optimization-on-the","title":"Efficient First-Order Optimization on the Pareto Set for Multi-Objective Learning under Preference Guidance","date":"2025-03-26","arxiv_id":"2504.02854","repositories_listed":0,"syntology":null},{"url":null,"slug":"decap-context-adaptive-prompt-generation-for","title":"DeCAP: Context-Adaptive Prompt Generation for Debiasing Zero-shot Question Answering in Large Language Models","date":"2025-03-25","arxiv_id":"2503.19426","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforcing-clinical-decision-support-through","title":"Reinforcing Clinical Decision Support through Multi-Agent Systems and Ethical AI Governance","date":"2025-03-25","arxiv_id":"2504.03699","repositories_listed":0,"syntology":null},{"url":null,"slug":"why-representation-engineering-works-a","title":"Why Representation Engineering Works: A Theoretical and Empirical Study in Vision-Language Models","date":"2025-03-25","arxiv_id":"2503.22720","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-bias-in-llms-for-job-resume","title":"Evaluating Bias in LLMs for Job-Resume Matching: Gender, Race, and Education","date":"2025-03-24","arxiv_id":"2503.19182","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-responsible-ai-music-an-investigation","title":"Towards Responsible AI Music: an Investigation of Trustworthy Features for Creative Systems","date":"2025-03-24","arxiv_id":"2503.18814","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-energy-landscapes-for-minimal","title":"Exploring Energy Landscapes for Minimal Counterfactual Explanations: Applications in Cybersecurity and Beyond","date":"2025-03-23","arxiv_id":"2503.18185","repositories_listed":0,"syntology":null},{"url":null,"slug":"frog-fair-removal-on-graphs","title":"FROG: Fair Removal on Graphs","date":"2025-03-23","arxiv_id":"2503.18197","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-origins-of-sampling-bias-implications","title":"On the Origins of Sampling Bias: Implications on Fairness Measurement and Mitigation","date":"2025-03-23","arxiv_id":"2503.17956","repositories_listed":0,"syntology":null},{"url":null,"slug":"bandwidth-reservation-for-time-critical","title":"Bandwidth Reservation for Time-Critical Vehicular Applications: A Multi-Operator Environment","date":"2025-03-22","arxiv_id":"2503.17756","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-driven-llm-based-causal-discovery","title":"Fairness-Driven LLM-based Causal Discovery with Active Learning and Dynamic Scoring","date":"2025-03-21","arxiv_id":"2503.17569","repositories_listed":0,"syntology":null},{"url":null,"slug":"principal-eigenvalue-regularization-for","title":"Principal Eigenvalue Regularization for Improved Worst-Class Certified Robustness of Smoothed Classifiers","date":"2025-03-21","arxiv_id":"2503.17172","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-llm-guardrails-via-sparse","title":"Towards LLM Guardrails via Sparse Representation Steering","date":"2025-03-21","arxiv_id":"2503.16851","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-pruning-automated-fairness-repair","title":"Attention Pruning: Automated Fairness Repair of Language Models via Surrogate Simulated Annealing","date":"2025-03-20","arxiv_id":"2503.15815","repositories_listed":0,"syntology":null},{"url":null,"slug":"empirical-analysis-of-privacy-fairness","title":"Empirical Analysis of Privacy-Fairness-Accuracy Trade-offs in Federated Learning: A Step Towards Responsible AI","date":"2025-03-20","arxiv_id":"2503.16233","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-structured-prompts-to-open-narratives","title":"From Structured Prompts to Open Narratives: Measuring Gender Bias in LLMs Through Open-Ended Storytelling","date":"2025-03-20","arxiv_id":"2503.15904","repositories_listed":0,"syntology":null},{"url":null,"slug":"partial-identification-in-moment-models-with","title":"Partial Identification in Moment Models with Incomplete Data via Optimal Transport","date":"2025-03-20","arxiv_id":"2503.16098","repositories_listed":0,"syntology":null},{"url":null,"slug":"resfl-an-uncertainty-aware-framework-for","title":"RESFL: An Uncertainty-Aware Framework for Responsible Federated Learning by Balancing Privacy, Fairness and Utility in Autonomous Vehicles","date":"2025-03-20","arxiv_id":"2503.16251","repositories_listed":0,"syntology":null},{"url":null,"slug":"are-elites-meritocratic-and-efficiency","title":"Are Elites Meritocratic and Efficiency-Seeking? Evidence from MBA Students","date":"2025-03-19","arxiv_id":"2503.15443","repositories_listed":0,"syntology":null},{"url":null,"slug":"enforcing-consistency-and-fairness-in-multi","title":"Enforcing Consistency and Fairness in Multi-level Hierarchical Classification with a Mask-based Output Layer","date":"2025-03-19","arxiv_id":"2503.15566","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-bias-in-retrieval-augmented","title":"Bias Evaluation and Mitigation in Retrieval-Augmented Medical Question-Answering Systems","date":"2025-03-19","arxiv_id":"2503.15454","repositories_listed":0,"syntology":null},{"url":null,"slug":"mmdt-decoding-the-trustworthiness-and-safety","title":"MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models","date":"2025-03-19","arxiv_id":"2503.14827","repositories_listed":0,"syntology":null},{"url":null,"slug":"pfedfair-towards-optimal-group-fairness","title":"pFedFair: Towards Optimal Group Fairness-Accuracy Trade-off in Heterogeneous Federated Learning","date":"2025-03-19","arxiv_id":"2503.14925","repositories_listed":0,"syntology":null},{"url":null,"slug":"gender-and-content-bias-in-large-language","title":"Gender and content bias in Large Language Models: a case study on Google Gemini 2.0 Flash Experimental","date":"2025-03-18","arxiv_id":"2503.16534","repositories_listed":0,"syntology":null},{"url":null,"slug":"cohort-attention-evaluation-metric-against","title":"Cohort-attention Evaluation Metric against Tied Data: Studying Performance of Classification Models in Cancer Detection","date":"2025-03-17","arxiv_id":"2503.12755","repositories_listed":0,"syntology":null},{"url":null,"slug":"okra-an-explainable-heterogeneous-multi","title":"OKRA: an Explainable, Heterogeneous, Multi-Stakeholder Job Recommender System","date":"2025-03-17","arxiv_id":"2504.07108","repositories_listed":0,"syntology":null},{"url":null,"slug":"vericontaminated-assessing-llm-driven-verilog","title":"VeriContaminated: Assessing LLM-Driven Verilog Coding for Data Contamination","date":"2025-03-17","arxiv_id":"2503.13572","repositories_listed":0,"syntology":null},{"url":null,"slug":"debiasing-diffusion-model-enhancing-fairness","title":"Debiasing Diffusion Model: Enhancing Fairness through Latent Representation Learning in Stable Diffusion Model","date":"2025-03-16","arxiv_id":"2503.12536","repositories_listed":0,"syntology":null},{"url":null,"slug":"negotiative-alignment-embracing-disagreement","title":"Negotiative Alignment: Embracing Disagreement to Achieve Fairer Outcomes -- Insights from Urban Studies","date":"2025-03-16","arxiv_id":"2503.12613","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-min-max-optimization-via-primal-dual","title":"Scalable Min-Max Optimization via Primal-Dual Exact Pareto Optimization","date":"2025-03-16","arxiv_id":"2504.02833","repositories_listed":0,"syntology":null},{"url":null,"slug":"unveiling-pitfalls-understanding-why-ai","title":"Unveiling Pitfalls: Understanding Why AI-driven Code Agents Fail at GitHub Issue Resolution","date":"2025-03-16","arxiv_id":"2503.12374","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedtilt-towards-multi-level-fairness","title":"FedTilt: Towards Multi-Level Fairness-Preserving and Robust Federated Learning","date":"2025-03-15","arxiv_id":"2503.13537","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-hybrid-transmit-beamforming-for-mm","title":"Optimal Hybrid Transmit Beamforming for mm-Wave Integrated Sensing and Communication","date":"2025-03-15","arxiv_id":"2503.12129","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-objective-evaluation-framework-for","title":"A Multi-Objective Evaluation Framework for Analyzing Utility-Fairness Trade-Offs in Machine Learning Systems","date":"2025-03-14","arxiv_id":"2503.11120","repositories_listed":0,"syntology":null},{"url":null,"slug":"llm-agents-for-education-advances-and","title":"LLM Agents for Education: Advances and Applications","date":"2025-03-14","arxiv_id":"2503.11733","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedpca-noise-robust-fair-federated-learning","title":"FedPCA: Noise-Robust Fair Federated Learning via Performance-Capacity Analysis","date":"2025-03-13","arxiv_id":"2503.10567","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-trustworthiness-challenges-in","title":"Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction","date":"2025-03-13","arxiv_id":"2503.09947","repositories_listed":0,"syntology":null},{"url":null,"slug":"pluralllm-pluralistic-alignment-in-llms-via","title":"PluralLLM: Pluralistic Alignment in LLMs via Federated Learning","date":"2025-03-13","arxiv_id":"2503.09925","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-role-of-hyperparameters-in-predictive","title":"The Role of Hyperparameters in Predictive Multiplicity","date":"2025-03-13","arxiv_id":"2503.13506","repositories_listed":0,"syntology":null},{"url":null,"slug":"equipy-sequential-fairness-using-optimal","title":"EquiPy: Sequential Fairness using Optimal Transport in Python","date":"2025-03-12","arxiv_id":"2503.09866","repositories_listed":0,"syntology":null},{"url":null,"slug":"mitigating-membership-inference-vulnerability","title":"Mitigating Membership Inference Vulnerability in Personalized Federated Learning","date":"2025-03-12","arxiv_id":"2503.09414","repositories_listed":0,"syntology":null},{"url":null,"slug":"scihorizon-benchmarking-ai-for-science","title":"SciHorizon: Benchmarking AI-for-Science Readiness from Scientific Data to Large Language Models","date":"2025-03-12","arxiv_id":"2503.13503","repositories_listed":0,"syntology":null},{"url":null,"slug":"cad-vae-leveraging-correlation-aware-latents","title":"CAD-VAE: Leveraging Correlation-Aware Latents for Comprehensive Fair Disentanglement","date":"2025-03-11","arxiv_id":"2503.07938","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-and-accurate-estimation-of-1","title":"Efficient and Accurate Estimation of Lipschitz Constants for Hybrid Quantum-Classical Decision Models","date":"2025-03-11","arxiv_id":"2503.07992","repositories_listed":0,"syntology":null},{"url":null,"slug":"exposing-product-bias-in-llm-investment","title":"Exposing Product Bias in LLM Investment Recommendation","date":"2025-03-11","arxiv_id":"2503.08750","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-pareto-manifolds-in-high-dimensions","title":"Learning Pareto manifolds in high dimensions: How can regularization help?","date":"2025-03-11","arxiv_id":"2503.08849","repositories_listed":0,"syntology":null},{"url":null,"slug":"llms-virtual-users-and-bias-predicting-any","title":"Llms, Virtual Users, and Bias: Predicting Any Survey Question Without Human Data","date":"2025-03-11","arxiv_id":"2503.16498","repositories_listed":0,"syntology":null},{"url":null,"slug":"reconfigurable-intelligent-sensing-surface-1","title":"Reconfigurable Intelligent Sensing Surface enables Wireless Powered Communication Networks: Interference Suppression and Massive Wireless Energy Transfer","date":"2025-03-11","arxiv_id":"2503.08198","repositories_listed":0,"syntology":null},{"url":null,"slug":"sublinear-algorithms-for-wasserstein-and","title":"Sublinear Algorithms for Wasserstein and Total Variation Distances: Applications to Fairness and Privacy Auditing","date":"2025-03-10","arxiv_id":"2503.07775","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-influence-of-missing-data-mechanisms-and","title":"The influence of missing data mechanisms and simple missing data handling techniques on fairness","date":"2025-03-10","arxiv_id":"2503.07313","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-large-language-models-that-benefit","title":"Towards Large Language Models that Benefit for All: Benchmarking Group Fairness in Reward Models","date":"2025-03-10","arxiv_id":"2503.07806","repositories_listed":0,"syntology":null},{"url":null,"slug":"trustworthy-machine-learning-via-memorization","title":"Trustworthy Machine Learning via Memorization and the Granular Long-Tail: A Survey on Interactions, Tradeoffs, and Beyond","date":"2025-03-10","arxiv_id":"2503.07501","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-quantitative-evaluation-of-the-expressivity","title":"A Quantitative Evaluation of the Expressivity of BMI, Pose and Gender in Body Embeddings for Recognition and Identification","date":"2025-03-09","arxiv_id":"2503.06451","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-aware-organ-exchange-and-kidney","title":"Fairness-aware organ exchange and kidney paired donation","date":"2025-03-09","arxiv_id":"2503.06431","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-frank-system-for-co-evolutionary-hybrid","title":"A Frank System for Co-Evolutionary Hybrid Decision-Making","date":"2025-03-08","arxiv_id":"2503.06229","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-fairness-interventions-come-at-the-cost-of","title":"Do Fairness Interventions Come at the Cost of Privacy: Evaluations for Binary Classifiers","date":"2025-03-08","arxiv_id":"2503.06150","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-bias-detection-in-llm-enhancing","title":"Fine-Grained Bias Detection in LLM: Enhancing detection mechanisms for nuanced biases","date":"2025-03-08","arxiv_id":"2503.06054","repositories_listed":0,"syntology":null},{"url":null,"slug":"gradient-driven-graph-neural-networks-for","title":"Gradient-Driven Graph Neural Networks for Learning Digital and Hybrid Precoder","date":"2025-03-08","arxiv_id":"2503.06077","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-ai-pentad-the-charme-2-d-model-and-an","title":"The AI Pentad, the CHARME$^{2}$D Model, and an Assessment of Current-State AI Regulation","date":"2025-03-08","arxiv_id":"2503.06353","repositories_listed":0,"syntology":null},{"url":null,"slug":"autotestforge-a-multidimensional-automated","title":"AutoTestForge: A Multidimensional Automated Testing Framework for Natural Language Processing Models","date":"2025-03-07","arxiv_id":"2503.05102","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-aware-low-rank-adaptation-under","title":"Fairness-Aware Low-Rank Adaptation Under Demographic Privacy Constraints","date":"2025-03-07","arxiv_id":"2503.05684","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-pricing-for-on-demand-dnn-inference","title":"Dynamic Pricing for On-Demand DNN Inference in the Edge-AI Market","date":"2025-03-06","arxiv_id":"2503.04521","repositories_listed":0,"syntology":null},{"url":null,"slug":"precoder-learning-for-weighted-sum-rate","title":"Precoder Learning for Weighted Sum Rate Maximization","date":"2025-03-06","arxiv_id":"2503.04497","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-implicit-preference-based-policy-fine","title":"Human Implicit Preference-Based Policy Fine-tuning for Multi-Agent Reinforcement Learning in USV Swarm","date":"2025-03-05","arxiv_id":"2503.03796","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-augmentation-in-federation","title":"Knowledge Augmentation in Federation: Rethinking What Collaborative Learning Can Bring Back to Decentralized Data","date":"2025-03-05","arxiv_id":"2503.03140","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-trustworthy-federated-learning","title":"Towards Trustworthy Federated Learning","date":"2025-03-05","arxiv_id":"2503.03684","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformer-based-power-optimization-for-max","title":"Transformer-Based Power Optimization for Max-Min Fairness in Cell-Free Massive MIMO","date":"2025-03-05","arxiv_id":"2503.03561","repositories_listed":0,"syntology":null},{"url":null,"slug":"echoqa-a-large-collection-of-instruction","title":"EchoQA: A Large Collection of Instruction Tuning Data for Echocardiogram Reports","date":"2025-03-04","arxiv_id":"2503.02365","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-play-in-the-fast-lane-integrating","title":"Fair Play in the Fast Lane: Integrating Sportsmanship into Autonomous Racing Systems","date":"2025-03-04","arxiv_id":"2503.03774","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairsense-ai-responsible-ai-meets","title":"FairSense-AI: Responsible AI Meets Sustainability","date":"2025-03-04","arxiv_id":"2503.02865","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiaccuracy-and-multicalibration-via-proxy","title":"Multiaccuracy and Multicalibration via Proxy Groups","date":"2025-03-04","arxiv_id":"2503.02870","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-preserving-fair-synthetic-tabular","title":"Privacy-Preserving Fair Synthetic Tabular Data","date":"2025-03-04","arxiv_id":"2503.02968","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-explainable-doctor-recommendation","title":"Towards Explainable Doctor Recommendation with Large Language Models","date":"2025-03-04","arxiv_id":"2503.02298","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-reinforcement-learning-based-user-1","title":"Deep Reinforcement Learning-Based User Association in Hybrid LiFi/WiFi Indoor Networks","date":"2025-03-03","arxiv_id":"2503.01803","repositories_listed":0,"syntology":null},{"url":null,"slug":"mab-based-channel-scheduling-for-asynchronous","title":"MAB-Based Channel Scheduling for Asynchronous Federated Learning in Non-Stationary Environments","date":"2025-03-03","arxiv_id":"2503.01324","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-multi-stakeholder-evaluation-of-ml","title":"Towards Multi-Stakeholder Evaluation of ML Models: A Crowdsourcing Study on Metric Preferences in Job-matching System","date":"2025-03-03","arxiv_id":"2503.05796","repositories_listed":0,"syntology":null},{"url":null,"slug":"keynesian-beauty-contest-in-morocco-s-public","title":"Keynesian Beauty Contest in Morocco's Public Procurement Reform","date":"2025-03-02","arxiv_id":"2503.00883","repositories_listed":0,"syntology":null},{"url":null,"slug":"traffic-priority-aware-5g-nr-u-wi-fi","title":"Traffic Priority-Aware 5G NR-U/Wi-Fi Coexistence with Deep Reinforcement Learning","date":"2025-03-01","arxiv_id":"2503.00256","repositories_listed":0,"syntology":null},{"url":null,"slug":"2503-00234","title":"Investigating the Relationship Between Debiasing and Artifact Removal using Saliency Maps","date":"2025-02-28","arxiv_id":"2503.00234","repositories_listed":0,"syntology":null},{"url":null,"slug":"causality-is-key-to-understand-and-balance","title":"Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models","date":"2025-02-28","arxiv_id":"2502.21123","repositories_listed":0,"syntology":null},{"url":null,"slug":"probench-benchmarking-large-language-models","title":"ProBench: Benchmarking Large Language Models in Competitive Programming","date":"2025-02-28","arxiv_id":"2502.20868","repositories_listed":0,"syntology":null},{"url":null,"slug":"xaixarts-manifesto-explainable-ai-for-the","title":"XAIxArts Manifesto: Explainable AI for the Arts","date":"2025-02-28","arxiv_id":"2502.21220","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-language-model-strategic-reasoning","title":"Large Language Model Strategic Reasoning Evaluation through Behavioral Game Theory","date":"2025-02-27","arxiv_id":"2502.20432","repositories_listed":0,"syntology":null},{"url":null,"slug":"mapping-trustworthiness-in-large-language","title":"Mapping Trustworthiness in Large Language Models: A Bibliometric Analysis Bridging Theory to Practice","date":"2025-02-27","arxiv_id":"2503.04785","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-does-a-predictor-know-its-own-loss","title":"When does a predictor know its own loss?","date":"2025-02-27","arxiv_id":"2502.20375","repositories_listed":0,"syntology":null},{"url":null,"slug":"pilot-and-data-power-control-for-uplink-cell","title":"Pilot and Data Power Control for Uplink Cell-free massive MIMO","date":"2025-02-26","arxiv_id":"2502.19282","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-overview-of-large-language-models-for","title":"An Overview of Large Language Models for Statisticians","date":"2025-02-25","arxiv_id":"2502.17814","repositories_listed":0,"syntology":null},{"url":null,"slug":"defining-bias-in-ai-systems-biased-models-are","title":"Defining bias in AI-systems: Biased models are fair models","date":"2025-02-25","arxiv_id":"2502.18060","repositories_listed":0,"syntology":null},{"url":null,"slug":"endive-a-cross-dialect-benchmark-for-fairness","title":"EnDive: A Cross-Dialect Benchmark for Fairness and Performance in Large Language Models","date":"2025-02-25","arxiv_id":"2504.07100","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-gender-disparities-in-automatic","title":"Exploring Gender Disparities in Automatic Speech Recognition Technology","date":"2025-02-25","arxiv_id":"2502.18434","repositories_listed":0,"syntology":null},{"url":null,"slug":"finp-fairness-in-privacy-in-federated","title":"FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk","date":"2025-02-25","arxiv_id":"2502.17748","repositories_listed":0,"syntology":null},{"url":null,"slug":"mafe-multi-agent-fair-environments-for","title":"MAFE: Multi-Agent Fair Environments for Decision-Making Systems","date":"2025-02-25","arxiv_id":"2502.18534","repositories_listed":0,"syntology":null},{"url":null,"slug":"unmasking-gender-bias-in-recommendation","title":"Unmasking Gender Bias in Recommendation Systems and Enhancing Category-Aware Fairness","date":"2025-02-25","arxiv_id":"2502.17921","repositories_listed":0,"syntology":null},{"url":null,"slug":"achieving-fair-pca-using-joint-eigenvalue","title":"Achieving Fair PCA Using Joint Eigenvalue Decomposition","date":"2025-02-24","arxiv_id":"2502.16933","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-foundation-models-for-medical-image","title":"Fair Foundation Models for Medical Image Analysis: Challenges and Perspectives","date":"2025-02-24","arxiv_id":"2502.16841","repositories_listed":0,"syntology":null},{"url":null,"slug":"vision-language-models-in-medicine","title":"Vision Language Models in Medicine","date":"2025-02-24","arxiv_id":"2503.01863","repositories_listed":0,"syntology":null},{"url":null,"slug":"pls-based-approach-for-fair-representation","title":"PLS-based approach for fair representation learning","date":"2025-02-22","arxiv_id":"2502.16263","repositories_listed":0,"syntology":null},{"url":null,"slug":"reproducibility-study-of-cooperation","title":"Reproducibility Study of Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation","date":"2025-02-22","arxiv_id":"2502.16242","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-survey-on-the-trustworthiness","title":"A Comprehensive Survey on the Trustworthiness of Large Language Models in Healthcare","date":"2025-02-21","arxiv_id":"2502.15871","repositories_listed":0,"syntology":null},{"url":null,"slug":"blockchain-based-framework-for-scalable-and","title":"Blockchain-based Framework for Scalable and Incentivized Federated Learning","date":"2025-02-20","arxiv_id":"2502.14170","repositories_listed":0,"syntology":null}],"record_sha256":"d3dbb2904b344e4766e95113fe84733944a4eb66eeed5af016fb0d74a57cae2b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}