{"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/25","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":25,"pages_in_order":57,"rows_per_page":100,"rows":[2401,2500],"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/24","next":"/task/fairness/papers/26","papers":[{"url":null,"slug":"trustworthy-xai-and-application","title":"Trustworthy XAI and Application","date":"2024-10-22","arxiv_id":"2410.17139","repositories_listed":0,"syntology":null},{"url":null,"slug":"finite-sample-and-distribution-free-fair","title":"Finite-Sample and Distribution-Free Fair Classification: Optimal Trade-off Between Excess Risk and Fairness, and the Cost of Group-Blindness","date":"2024-10-21","arxiv_id":"2410.16477","repositories_listed":0,"syntology":null},{"url":null,"slug":"whither-bias-goes-i-will-go-an-integrative","title":"Whither Bias Goes, I Will Go: An Integrative, Systematic Review of Algorithmic Bias Mitigation","date":"2024-10-21","arxiv_id":"2410.19003","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-augmentation-via-diffusion-model-to","title":"Data Augmentation via Diffusion Model to Enhance AI Fairness","date":"2024-10-20","arxiv_id":"2410.15470","repositories_listed":0,"syntology":null},{"url":null,"slug":"ethical-ai-in-retail-consumer-privacy-and","title":"Ethical AI in Retail: Consumer Privacy and Fairness","date":"2024-10-20","arxiv_id":"2410.15369","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-evaluation-with-item-response-theory","title":"Fairness Evaluation with Item Response Theory","date":"2024-10-20","arxiv_id":"2411.02414","repositories_listed":0,"syntology":null},{"url":null,"slug":"who-is-undercover-guiding-llms-to-explore","title":"Who is Undercover? Guiding LLMs to Explore Multi-Perspective Team Tactic in the Game","date":"2024-10-20","arxiv_id":"2410.15311","repositories_listed":0,"syntology":null},{"url":null,"slug":"addressing-blind-guessing-calibration-of","title":"Addressing Blind Guessing: Calibration of Selection Bias in Multiple-Choice Question Answering by Video Language Models","date":"2024-10-18","arxiv_id":"2410.14248","repositories_listed":0,"syntology":null},{"url":null,"slug":"labsafety-bench-benchmarking-llms-on-safety","title":"LabSafety Bench: Benchmarking LLMs on Safety Issues in Scientific Labs","date":"2024-10-18","arxiv_id":"2410.14182","repositories_listed":0,"syntology":null},{"url":null,"slug":"text-to-image-representativity-fairness","title":"Text-to-Image Representativity Fairness Evaluation Framework","date":"2024-10-18","arxiv_id":"2410.14201","repositories_listed":0,"syntology":null},{"url":null,"slug":"auditing-and-enforcing-conditional-fairness","title":"Auditing and Enforcing Conditional Fairness via Optimal Transport","date":"2024-10-17","arxiv_id":"2410.14029","repositories_listed":0,"syntology":null},{"url":null,"slug":"facesaliencyaug-mitigating-geographic-gender","title":"FaceSaliencyAug: Mitigating Geographic, Gender and Stereotypical Biases via Saliency-Based Data Augmentation","date":"2024-10-17","arxiv_id":"2410.14070","repositories_listed":0,"syntology":null},{"url":null,"slug":"pessimistic-evaluation","title":"Pessimistic Evaluation","date":"2024-10-17","arxiv_id":"2410.13680","repositories_listed":0,"syntology":null},{"url":null,"slug":"advancing-fairness-in-natural-language","title":"Advancing Fairness in Natural Language Processing: From Traditional Methods to Explainability","date":"2024-10-16","arxiv_id":"2410.12511","repositories_listed":0,"syntology":null},{"url":null,"slug":"benchmarkcards-large-language-model-and-risk","title":"BenchmarkCards: Large Language Model and Risk Reporting","date":"2024-10-16","arxiv_id":"2410.12974","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-clustering-for-data-summarization","title":"Fair Clustering for Data Summarization: Improved Approximation Algorithms and Complexity Insights","date":"2024-10-16","arxiv_id":"2410.12913","repositories_listed":0,"syntology":null},{"url":null,"slug":"first-person-fairness-in-chatbots","title":"First-Person Fairness in Chatbots","date":"2024-10-16","arxiv_id":"2410.19803","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-protected-attributes-to-consider","title":"Using Protected Attributes to Consider Fairness in Multi-Agent Systems","date":"2024-10-16","arxiv_id":"2410.12889","repositories_listed":0,"syntology":null},{"url":null,"slug":"bias-similarity-across-large-language-models","title":"Bias Similarity Across Large Language Models","date":"2024-10-15","arxiv_id":"2410.12010","repositories_listed":0,"syntology":null},{"url":null,"slug":"hairmony-fairness-aware-hairstyle","title":"Hairmony: Fairness-aware hairstyle classification","date":"2024-10-15","arxiv_id":"2410.11528","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-bias-in-facial-attribute","title":"Improving Bias in Facial Attribute Classification: A Combined Impact of KL Divergence induced Loss Function and Dual Attention","date":"2024-10-15","arxiv_id":"2410.11176","repositories_listed":0,"syntology":null},{"url":null,"slug":"measuring-spiritual-values-and-bias-of-large","title":"Measuring Spiritual Values and Bias of Large Language Models","date":"2024-10-15","arxiv_id":"2410.11647","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-fair-language-model-paradox","title":"The Fair Language Model Paradox","date":"2024-10-15","arxiv_id":"2410.11985","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-fair-graph-representation-learning-in","title":"Towards Fair Graph Representation Learning in Social Networks","date":"2024-10-15","arxiv_id":"2410.11493","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-unforeseen-data-properties-with","title":"Detecting Unforeseen Data Properties with Diffusion Autoencoder Embeddings using Spine MRI data","date":"2024-10-14","arxiv_id":"2410.10220","repositories_listed":0,"syntology":null},{"url":null,"slug":"gender-bias-of-llm-in-economics-an","title":"Gender Bias of LLM in Economics: An Existentialism Perspective","date":"2024-10-14","arxiv_id":"2410.19775","repositories_listed":0,"syntology":null},{"url":null,"slug":"will-the-inclusion-of-generated-data-amplify","title":"Will the Inclusion of Generated Data Amplify Bias Across Generations in Future Image Classification Models?","date":"2024-10-14","arxiv_id":"2410.10160","repositories_listed":0,"syntology":null},{"url":null,"slug":"facial-width-to-height-ratio-does-not-predict","title":"Facial Width-to-Height Ratio Does Not Predict Self-Reported Behavioral Tendencies","date":"2024-10-13","arxiv_id":"2410.09979","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-fourier-auditor-for-estimating","title":"Active Fourier Auditor for Estimating Distributional Properties of ML Models","date":"2024-10-10","arxiv_id":"2410.08111","repositories_listed":0,"syntology":null},{"url":null,"slug":"demoshapley-valuation-of-demonstrations-for","title":"DemoShapley: Valuation of Demonstrations for In-Context Learning","date":"2024-10-10","arxiv_id":"2410.07523","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-differentially-private-synthetic","title":"Evaluating Differentially Private Synthetic Data Generation in High-Stakes Domains","date":"2024-10-10","arxiv_id":"2410.08327","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-balance-altruism-and-self","title":"Learning to Balance Altruism and Self-interest Based on Empathy in Mixed-Motive Games","date":"2024-10-10","arxiv_id":"2410.07863","repositories_listed":0,"syntology":null},{"url":null,"slug":"no-free-lunch-retrieval-augmented-generation","title":"No Free Lunch: Retrieval-Augmented Generation Undermines Fairness in LLMs, Even for Vigilant Users","date":"2024-10-10","arxiv_id":"2410.07589","repositories_listed":0,"syntology":null},{"url":null,"slug":"rab-2-def-dynamic-and-explainable-defense","title":"RAB$^2$-DEF: Dynamic and explainable defense against adversarial attacks in Federated Learning to fair poor clients","date":"2024-10-10","arxiv_id":"2410.08244","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-is-left-after-distillation-how-knowledge","title":"What is Left After Distillation? How Knowledge Transfer Impacts Fairness and Bias","date":"2024-10-10","arxiv_id":"2410.08407","repositories_listed":0,"syntology":null},{"url":null,"slug":"collusion-detection-with-graph-neural","title":"Collusion Detection with Graph Neural Networks","date":"2024-10-09","arxiv_id":"2410.07091","repositories_listed":0,"syntology":null},{"url":null,"slug":"pfattack-stealthy-attack-bypassing-group","title":"PFAttack: Stealthy Attack Bypassing Group Fairness in Federated Learning","date":"2024-10-09","arxiv_id":"2410.06509","repositories_listed":0,"syntology":null},{"url":null,"slug":"ethical-leadership-in-the-age-of-ai","title":"Ethical Leadership in the Age of AI Challenges, Opportunities and Framework for Ethical Leadership","date":"2024-10-08","arxiv_id":"2410.18095","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairedu-a-multiple-regression-based-method","title":"FAIREDU: A Multiple Regression-Based Method for Enhancing Fairness in Machine Learning Models for Educational Applications","date":"2024-10-08","arxiv_id":"2410.06423","repositories_listed":0,"syntology":null},{"url":null,"slug":"skin-cancer-machine-learning-model-tone-bias","title":"Skin Cancer Machine Learning Model Tone Bias","date":"2024-10-08","arxiv_id":"2410.06385","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-repellency-for-shielded-generation-in","title":"Sparse Repellency for Shielded Generation in Text-to-image Diffusion Models","date":"2024-10-08","arxiv_id":"2410.06025","repositories_listed":0,"syntology":null},{"url":null,"slug":"stochastic-bandits-for-egalitarian-assignment","title":"Stochastic Bandits for Egalitarian Assignment","date":"2024-10-08","arxiv_id":"2410.05856","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-an-operational-responsible-ai","title":"Towards an Operational Responsible AI Framework for Learning Analytics in Higher Education","date":"2024-10-08","arxiv_id":"2410.05827","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-aware-fairness-adaptive","title":"Uncertainty-Aware Fairness-Adaptive Classification Trees","date":"2024-10-08","arxiv_id":"2410.05810","repositories_listed":0,"syntology":null},{"url":null,"slug":"legal-theory-for-pluralistic-alignment","title":"Rules, Cases, and Reasoning: Positivist Legal Theory as a Framework for Pluralistic AI Alignment","date":"2024-10-07","arxiv_id":"2410.17271","repositories_listed":0,"syntology":null},{"url":null,"slug":"post-hoc-study-of-climate-microtargeting-on","title":"Post-hoc Study of Climate Microtargeting on Social Media Ads with LLMs: Thematic Insights and Fairness Evaluation","date":"2024-10-07","arxiv_id":"2410.05401","repositories_listed":0,"syntology":null},{"url":null,"slug":"social-choice-for-heterogeneous-fairness-in","title":"Social Choice for Heterogeneous Fairness in Recommendation","date":"2024-10-06","arxiv_id":"2410.04551","repositories_listed":0,"syntology":null},{"url":null,"slug":"unveiling-the-impact-of-local-homophily-on","title":"Unveiling the Impact of Local Homophily on GNN Fairness: In-Depth Analysis and New Benchmarks","date":"2024-10-05","arxiv_id":"2410.04287","repositories_listed":0,"syntology":null},{"url":null,"slug":"group-fairness-in-peer-review-1","title":"Group Fairness in Peer Review","date":"2024-10-04","arxiv_id":"2410.03474","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-tma-based-transmissive-ris","title":"Towards TMA-Based Transmissive RIS Transceiver Enabled Downlink Communication Networks: A Consensus-ADMM Approach","date":"2024-10-04","arxiv_id":"2410.03243","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-decision-subjects-engagement","title":"Understanding Decision Subjects' Engagement with and Perceived Fairness of AI Models When Opportunities of Qualification Improvement Exist","date":"2024-10-04","arxiv_id":"2410.03126","repositories_listed":0,"syntology":null},{"url":null,"slug":"achieving-fairness-in-predictive-process","title":"Achieving Fairness in Predictive Process Analytics via Adversarial Learning","date":"2024-10-03","arxiv_id":"2410.02618","repositories_listed":0,"syntology":null},{"url":null,"slug":"harm-ratio-a-novel-and-versatile-fairness","title":"Harm Ratio: A Novel and Versatile Fairness Criterion","date":"2024-10-03","arxiv_id":"2410.02977","repositories_listed":0,"syntology":null},{"url":null,"slug":"minimax-group-fairness-in-strategic","title":"Minimax Group Fairness in Strategic Classification","date":"2024-10-03","arxiv_id":"2410.02513","repositories_listed":0,"syntology":null},{"url":null,"slug":"overcoming-representation-bias-in-fairness","title":"Overcoming Representation Bias in Fairness-Aware data Repair using Optimal Transport","date":"2024-10-03","arxiv_id":"2410.02840","repositories_listed":0,"syntology":null},{"url":null,"slug":"pfguard-a-generative-framework-with-privacy","title":"PFGuard: A Generative Framework with Privacy and Fairness Safeguards","date":"2024-10-03","arxiv_id":"2410.02246","repositories_listed":0,"syntology":null},{"url":null,"slug":"causal-inference-tools-for-a-better","title":"Causal Inference Tools for a Better Evaluation of Machine Learning","date":"2024-10-02","arxiv_id":"2410.01392","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-and-machine-learning-advancing-2","title":"Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Unveiling AI's Potential Through Tools, Techniques, and Applications","date":"2024-10-02","arxiv_id":"2410.01268","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-class-incremental-learning-using-sample","title":"Fair Class-Incremental Learning using Sample Weighting","date":"2024-10-02","arxiv_id":"2410.01324","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair4free-generating-high-fidelity-fair","title":"Fair4Free: Generating High-fidelity Fair Synthetic Samples using Data Free Distillation","date":"2024-10-02","arxiv_id":"2410.01423","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairlyuncertain-a-comprehensive-benchmark-of","title":"FairlyUncertain: A Comprehensive Benchmark of Uncertainty in Algorithmic Fairness","date":"2024-10-02","arxiv_id":"2410.02005","repositories_listed":0,"syntology":null},{"url":null,"slug":"proximix-enhancing-fairness-with-proximity","title":"ProxiMix: Enhancing Fairness with Proximity Samples in Subgroups","date":"2024-10-02","arxiv_id":"2410.01145","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-multi-stakeholder-job-recommender","title":"Explainable Multi-Stakeholder Job Recommender Systems","date":"2024-10-01","arxiv_id":"2410.00654","repositories_listed":0,"syntology":null},{"url":null,"slug":"fmbench-benchmarking-fairness-in-multimodal","title":"FMBench: Benchmarking Fairness in Multimodal Large Language Models on Medical Tasks","date":"2024-10-01","arxiv_id":"2410.01089","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-large-scale-operational-study-of","title":"A large-scale operational study of fingerprint quality and demographics","date":"2024-09-30","arxiv_id":"2409.19992","repositories_listed":0,"syntology":null},{"url":null,"slug":"positive-sum-fairness-leveraging-demographic","title":"Positive-Sum Fairness: Leveraging Demographic Attributes to Achieve Fair AI Outcomes Without Sacrificing Group Gains","date":"2024-09-30","arxiv_id":"2409.19940","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-analysis-with-shapley-owen-effects","title":"Fairness Analysis with Shapley-Owen Effects","date":"2024-09-28","arxiv_id":"2409.19318","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-textgcn-based-decoding-approach-for","title":"A TextGCN-Based Decoding Approach for Improving Remote Sensing Image Captioning","date":"2024-09-27","arxiv_id":"2409.18467","repositories_listed":0,"syntology":null},{"url":null,"slug":"differential-privacy-for-protecting-patient","title":"Differential privacy enables fair and accurate AI-based analysis of speech disorders while protecting patient data","date":"2024-09-27","arxiv_id":"2409.19078","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-fairness-driven-method-for-learning-human","title":"A Fairness-Driven Method for Learning Human-Compatible Negotiation Strategies","date":"2024-09-26","arxiv_id":"2409.18335","repositories_listed":0,"syntology":null},{"url":null,"slug":"behaviour4all-in-the-wild-facial-behaviour","title":"Behaviour4All: in-the-wild Facial Behaviour Analysis Toolkit","date":"2024-09-26","arxiv_id":"2409.17717","repositories_listed":0,"syntology":null},{"url":"/paper/efficient-fairness-performance-pareto-front","slug":"efficient-fairness-performance-pareto-front","title":"Efficient Fairness-Performance Pareto Front Computation","date":"2024-09-26","arxiv_id":"2409.17643","repositories_listed":0,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/efficient-fairness-performance-pareto-front#ran","syntology_url":"https://syntology.ai/paper/2409.17643","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.17643"}},"official":null}},{"url":null,"slug":"model-based-machine-learning-for-max-min","title":"Model-Based Machine Learning for Max-Min Fairness Beamforming Design in JCAS Systems","date":"2024-09-26","arxiv_id":"2409.17644","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-effective-robust-and-fairness-aware-hate","title":"An Effective, Robust and Fairness-aware Hate Speech Detection Framework","date":"2024-09-25","arxiv_id":"2409.17191","repositories_listed":0,"syntology":null},{"url":null,"slug":"managing-basis-risks-in-weather-parametric","title":"Managing Basis Risks in Weather Parametric Insurance: A Quantitative Study of Diversification and Key Influencing Factors","date":"2024-09-25","arxiv_id":"2409.16599","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-survey-of-bias-in-llms","title":"A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions","date":"2024-09-24","arxiv_id":"2409.16430","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-pixels-to-words-leveraging","title":"From Pixels to Words: Leveraging Explainability in Face Recognition through Interactive Natural Language Processing","date":"2024-09-24","arxiv_id":"2409.16089","repositories_listed":0,"syntology":null},{"url":null,"slug":"adapfair-ensuring-continuous-fairness-for","title":"AdapFair: Ensuring Continuous Fairness for Machine Learning Operations","date":"2024-09-23","arxiv_id":"2409.15088","repositories_listed":0,"syntology":null},{"url":null,"slug":"asking-an-ai-for-salary-negotiation-advice-is","title":"Asking an AI for salary negotiation advice is a matter of concern: Controlled experimental perturbation of ChatGPT for protected and non-protected group discrimination on a contextual task with no clear ground truth answers","date":"2024-09-23","arxiv_id":"2409.15567","repositories_listed":0,"syntology":null},{"url":null,"slug":"method-of-equal-shares-with-bounded","title":"Method of Equal Shares with Bounded Overspending","date":"2024-09-23","arxiv_id":"2409.15005","repositories_listed":0,"syntology":null},{"url":null,"slug":"not-only-the-last-layer-features-for-spurious","title":"Not Only the Last-Layer Features for Spurious Correlations: All Layer Deep Feature Reweighting","date":"2024-09-23","arxiv_id":"2409.14637","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-gender-racial-and-age-biases-in","title":"Evaluating Gender, Racial, and Age Biases in Large Language Models: A Comparative Analysis of Occupational and Crime Scenarios","date":"2024-09-22","arxiv_id":"2409.14583","repositories_listed":0,"syntology":null},{"url":null,"slug":"uav-enabled-data-collection-for-iot-networks","title":"UAV-Enabled Data Collection for IoT Networks via Rainbow Learning","date":"2024-09-22","arxiv_id":"2409.14521","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-generation-via-latent-factor-simulation","title":"Data Generation via Latent Factor Simulation for Fairness-aware Re-ranking","date":"2024-09-21","arxiv_id":"2409.14078","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-trade-off-between-data-minimization-and","title":"The trade-off between data minimization and fairness in collaborative filtering","date":"2024-09-21","arxiv_id":"2410.07182","repositories_listed":0,"syntology":null},{"url":null,"slug":"anti-jamming-transmission-of-downlink-cell","title":"Anti-jamming Transmission of Downlink Cell Free Millimeter-Wave MIMO System","date":"2024-09-20","arxiv_id":"2409.13261","repositories_listed":0,"syntology":null},{"url":null,"slug":"transforming-disaster-risk-reduction-with-ai","title":"Transforming disaster risk reduction with AI and big data: Legal and interdisciplinary perspectives","date":"2024-09-20","arxiv_id":"2410.07123","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-dive-into-fairness-bias-threats-and","title":"A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research","date":"2024-09-19","arxiv_id":"2409.12651","repositories_listed":0,"syntology":null},{"url":null,"slug":"is-it-still-fair-a-comparative-evaluation-of","title":"Is it Still Fair? A Comparative Evaluation of Fairness Algorithms through the Lens of Covariate Drift","date":"2024-09-19","arxiv_id":"2409.12428","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-data-envelopment-analysis-approach-for","title":"A Data Envelopment Analysis Approach for Assessing Fairness in Resource Allocation: Application to Kidney Exchange Programs","date":"2024-09-18","arxiv_id":"2410.02799","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffairec-generative-fair-recommender-with","title":"DifFaiRec: Generative Fair Recommender with Conditional Diffusion Model","date":"2024-09-18","arxiv_id":"2410.02791","repositories_listed":0,"syntology":null},{"url":null,"slug":"user-subgrouping-and-power-control-for","title":"User Subgrouping and Power Control for Multicast Massive MIMO over Spatially Correlated Channels","date":"2024-09-18","arxiv_id":"2409.11891","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-approach-for-user-centric","title":"A Deep Learning Approach for User-Centric Clustering in Cell-Free Massive MIMO Systems","date":"2024-09-17","arxiv_id":"2410.02775","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-algorithmic-fairness-a-guide-to","title":"Beyond Algorithmic Fairness: A Guide to Develop and Deploy Ethical AI-Enabled Decision-Support Tools","date":"2024-09-17","arxiv_id":"2409.11489","repositories_listed":0,"syntology":null},{"url":null,"slug":"bypassing-the-popularity-bias-repurposing","title":"Bypassing the Popularity Bias: Repurposing Models for Better Long-Tail Recommendation","date":"2024-09-17","arxiv_id":"2410.02776","repositories_listed":0,"syntology":null},{"url":null,"slug":"challenging-fairness-a-comprehensive","title":"Unveiling and Mitigating Bias in Large Language Model Recommendations: A Path to Fairness","date":"2024-09-17","arxiv_id":"2409.10825","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-anomaly-detection-for-imbalanced-groups","title":"Fair Anomaly Detection For Imbalanced Groups","date":"2024-09-17","arxiv_id":"2409.10951","repositories_listed":0,"syntology":null},{"url":null,"slug":"oath-efficient-and-flexible-zero-knowledge","title":"OATH: Efficient and Flexible Zero-Knowledge Proofs of End-to-End ML Fairness","date":"2024-09-17","arxiv_id":"2410.02777","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-green-multi-attribute-client-selection-for","title":"A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approach","date":"2024-09-16","arxiv_id":"2409.11442","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-not-emotion-drives-socioeconomic","title":"Fairness, not Emotion, Drives Socioeconomic Decision Making","date":"2024-09-16","arxiv_id":"2409.10322","repositories_listed":0,"syntology":null}],"record_sha256":"f79a074caa573ca4ab79db7a61b7dbade11b536c430c999a601f04719c026733","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}