{"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/18","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":18,"pages_in_order":57,"rows_per_page":100,"rows":[1701,1800],"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/17","next":"/task/fairness/papers/19","papers":[{"url":"/paper/fair-and-diverse-dpp-based-data-summarization","slug":"fair-and-diverse-dpp-based-data-summarization","title":"Fair and Diverse DPP-based Data Summarization","date":"2018-02-12","arxiv_id":"1802.04023","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"0 ran · 2 unverified","sample_list":"/paper/fair-and-diverse-dpp-based-data-summarization#ran","syntology_url":"https://syntology.ai/paper/1802.04023","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.04023"}},"official":null}},{"url":"/paper/does-mitigating-mls-impact-disparity-require","slug":"does-mitigating-mls-impact-disparity-require","title":"Does mitigating ML's impact disparity require treatment disparity?","date":"2017-11-19","arxiv_id":"1711.07076","repositories_listed":1,"syntology":null},{"url":"/paper/predict-responsibly-improving-fairness-and","slug":"predict-responsibly-improving-fairness-and","title":"Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer","date":"2017-11-17","arxiv_id":"1711.06664","repositories_listed":1,"syntology":null},{"url":"/paper/multiwinner-voting-with-fairness-constraints","slug":"multiwinner-voting-with-fairness-constraints","title":"Multiwinner Voting with Fairness Constraints","date":"2017-10-27","arxiv_id":"1710.10057","repositories_listed":1,"syntology":null},{"url":"/paper/provably-fair-representations","slug":"provably-fair-representations","title":"Provably Fair Representations","date":"2017-10-12","arxiv_id":"1710.04394","repositories_listed":1,"syntology":null},{"url":"/paper/on-fairness-and-calibration","slug":"on-fairness-and-calibration","title":"On Fairness and Calibration","date":"2017-09-06","arxiv_id":"1709.02012","repositories_listed":1,"syntology":null},{"url":"/paper/from-parity-to-preference-based-notions-of","slug":"from-parity-to-preference-based-notions-of","title":"From Parity to Preference-based Notions of Fairness in Classification","date":"2017-06-30","arxiv_id":"1707.00010","repositories_listed":1,"syntology":null},{"url":"/paper/a-convex-framework-for-fair-regression","slug":"a-convex-framework-for-fair-regression","title":"A Convex Framework for Fair Regression","date":"2017-06-07","arxiv_id":"1706.02409","repositories_listed":1,"syntology":null},{"url":"/paper/beyond-parity-fairness-objectives-for","slug":"beyond-parity-fairness-objectives-for","title":"Beyond Parity: Fairness Objectives for Collaborative Filtering","date":"2017-05-24","arxiv_id":"1705.08804","repositories_listed":1,"syntology":null},{"url":"/paper/diverse-weighted-bipartite-b-matching","slug":"diverse-weighted-bipartite-b-matching","title":"Diverse Weighted Bipartite b-Matching","date":"2017-02-23","arxiv_id":"1702.07134","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/diverse-weighted-bipartite-b-matching#ran","syntology_url":"https://syntology.ai/paper/1702.07134","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1702.07134"}},"official":{"repos":["faezahmed/diverse_matching"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/efficient-policy-learning","slug":"efficient-policy-learning","title":"Policy Learning with Observational Data","date":"2017-02-09","arxiv_id":"1702.02896","repositories_listed":1,"syntology":null},{"url":"/paper/iterative-orthogonal-feature-projection-for","slug":"iterative-orthogonal-feature-projection-for","title":"Iterative Orthogonal Feature Projection for Diagnosing Bias in Black-Box Models","date":"2016-11-15","arxiv_id":"1611.04967","repositories_listed":1,"syntology":null},{"url":"/paper/a-confidence-based-approach-for-balancing","slug":"a-confidence-based-approach-for-balancing","title":"A Confidence-Based Approach for Balancing Fairness and Accuracy","date":"2016-01-21","arxiv_id":"1601.05764","repositories_listed":1,"syntology":null},{"url":"/paper/censoring-representations-with-an-adversary","slug":"censoring-representations-with-an-adversary","title":"Censoring Representations with an Adversary","date":"2015-11-18","arxiv_id":"1511.05897","repositories_listed":1,"syntology":null},{"url":null,"slug":"fedga-a-fair-federated-learning-framework","title":"FedGA: A Fair Federated Learning Framework Based on the Gini Coefficient","date":"2025-07-17","arxiv_id":"2507.12983","repositories_listed":0,"syntology":null},{"url":null,"slug":"fade-adversarial-concept-erasure-in-flow","title":"FADE: Adversarial Concept Erasure in Flow Models","date":"2025-07-16","arxiv_id":"2507.12283","repositories_listed":0,"syntology":null},{"url":null,"slug":"looking-for-fairness-in-recommender-systems","title":"Looking for Fairness in Recommender Systems","date":"2025-07-16","arxiv_id":"2507.12242","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-aware-secure-integrated-sensing-and","title":"Fairness-Aware Secure Integrated Sensing and Communications with Fractional Programming","date":"2025-07-15","arxiv_id":"2507.11224","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-reinforcement-learning-for-fast-and-data","title":"Meta-Reinforcement Learning for Fast and Data-Efficient Spectrum Allocation in Dynamic Wireless Networks","date":"2025-07-13","arxiv_id":"2507.10619","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-collaborative-fairness-in-federated","title":"Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift","date":"2025-07-11","arxiv_id":"2507.08617","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-learning","title":"Graph Learning","date":"2025-07-08","arxiv_id":"2507.05636","repositories_listed":0,"syntology":null},{"url":null,"slug":"lumicrs-asymmetric-contrastive-prototype","title":"LumiCRS: Asymmetric Contrastive Prototype Learning for Long-Tail Conversational Movie Recommendation","date":"2025-07-07","arxiv_id":"2507.04722","repositories_listed":0,"syntology":null},{"url":null,"slug":"bifair-a-fairness-aware-training-framework","title":"BiFair: A Fairness-aware Training Framework for LLM-enhanced Recommender Systems via Bi-level Optimization","date":"2025-07-06","arxiv_id":"2507.04294","repositories_listed":0,"syntology":null},{"url":null,"slug":"indianbailjudgments-1200-a-multi-attribute","title":"IndianBailJudgments-1200: A Multi-Attribute Dataset for Legal NLP on Indian Bail Orders","date":"2025-07-03","arxiv_id":"2507.02506","repositories_listed":0,"syntology":null},{"url":null,"slug":"is-reasoning-all-you-need-probing-bias-in-the","title":"Is Reasoning All You Need? Probing Bias in the Age of Reasoning Language Models","date":"2025-07-03","arxiv_id":"2507.02799","repositories_listed":0,"syntology":null},{"url":null,"slug":"mateinfoub-a-real-world-benchmark-for-testing","title":"MateInfoUB: A Real-World Benchmark for Testing LLMs in Competitive, Multilingual, and Multimodal Educational Tasks","date":"2025-07-03","arxiv_id":"2507.03162","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-the-promise-and-pitfalls-of-llms","title":"Evaluating the Promise and Pitfalls of LLMs in Hiring Decisions","date":"2025-07-02","arxiv_id":"2507.02087","repositories_listed":0,"syntology":null},{"url":null,"slug":"why-multi-interest-fairness-matters","title":"Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System","date":"2025-07-01","arxiv_id":"2507.02000","repositories_listed":0,"syntology":null},{"url":null,"slug":"artificial-delegates-resolve-fairness-issues","title":"Artificial Delegates Resolve Fairness Issues in Perpetual Voting with Partial Turnout","date":"2025-06-26","arxiv_id":"2506.21186","repositories_listed":0,"syntology":null},{"url":null,"slug":"feda4fair-client-level-federated-datasets-for","title":"FeDa4Fair: Client-Level Federated Datasets for Fairness Evaluation","date":"2025-06-26","arxiv_id":"2506.21095","repositories_listed":0,"syntology":null},{"url":null,"slug":"demonstration-of-effective-ucb-based-routing","title":"Demonstration of effective UCB-based routing in skill-based queues on real-world data","date":"2025-06-25","arxiv_id":"2506.20543","repositories_listed":0,"syntology":null},{"url":null,"slug":"producer-fairness-in-sequential-bundle","title":"Producer-Fairness in Sequential Bundle Recommendation","date":"2025-06-25","arxiv_id":"2506.20329","repositories_listed":0,"syntology":null},{"url":null,"slug":"causal-aware-intelligent-qoe-optimization-for","title":"Causal-Aware Intelligent QoE Optimization for VR Interaction with Adaptive Keyframe Extraction","date":"2025-06-24","arxiv_id":"2506.19890","repositories_listed":0,"syntology":null},{"url":null,"slug":"peer-to-peer-energy-markets-with-uniform","title":"Peer-to-Peer Energy Markets With Uniform Pricing: A Dynamic Operating Envelope Approach","date":"2025-06-24","arxiv_id":"2506.19328","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-visual-image-based-user","title":"Multimodal Visual Image Based User Association and Beamforming Using Graph Neural Networks","date":"2025-06-23","arxiv_id":"2506.18218","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantifying-fairness-in-llms-beyond-tokens-a","title":"Quantifying Fairness in LLMs Beyond Tokens: A Semantic and Statistical Perspective","date":"2025-06-23","arxiv_id":"2506.19028","repositories_listed":0,"syntology":null},{"url":null,"slug":"reading-smiles-proxy-bias-in-foundation","title":"Reading Smiles: Proxy Bias in Foundation Models for Facial Emotion Recognition","date":"2025-06-23","arxiv_id":"2506.19079","repositories_listed":0,"syntology":null},{"url":null,"slug":"visibly-fair-mechanisms","title":"Visibly Fair Mechanisms","date":"2025-06-23","arxiv_id":"2506.19176","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-axiomatization-of-the-random-priority-rule","title":"An Axiomatization of the Random Priority Rule","date":"2025-06-22","arxiv_id":"2506.17997","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-based-content-creation-and-product","title":"AI based Content Creation and Product Recommendation Applications in E-commerce: An Ethical overview","date":"2025-06-20","arxiv_id":"2506.17370","repositories_listed":0,"syntology":null},{"url":null,"slug":"client-selection-strategies-for-federated","title":"Client Selection Strategies for Federated Semantic Communications in Heterogeneous IoT Networks","date":"2025-06-20","arxiv_id":"2506.17063","repositories_listed":0,"syntology":null},{"url":null,"slug":"soft-decision-trees-for-survival-analysis","title":"Soft decision trees for survival analysis","date":"2025-06-20","arxiv_id":"2506.16846","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-is-the-point-of-equality-in-machine","title":"What Is the Point of Equality in Machine Learning Fairness? Beyond Equality of Opportunity","date":"2025-06-20","arxiv_id":"2506.16782","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-contracts-in-principal-agent-games-with","title":"Fair Contracts in Principal-Agent Games with Heterogeneous Types","date":"2025-06-18","arxiv_id":"2506.15887","repositories_listed":0,"syntology":null},{"url":null,"slug":"compositional-attribute-imbalance-in-vision","title":"Compositional Attribute Imbalance in Vision Datasets","date":"2025-06-17","arxiv_id":"2506.14418","repositories_listed":0,"syntology":null},{"url":null,"slug":"computational-studies-in-influencer-marketing","title":"Computational Studies in Influencer Marketing: A Systematic Literature Review","date":"2025-06-17","arxiv_id":"2506.14602","repositories_listed":0,"syntology":null},{"url":null,"slug":"convergence-privacy-fairness-trade-off-in","title":"Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning","date":"2025-06-17","arxiv_id":"2506.14251","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-for-a-few-improving-fairness-in-doubly","title":"Fair for a few: Improving Fairness in Doubly Imbalanced Datasets","date":"2025-06-17","arxiv_id":"2506.14306","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-size-fits-none-rethinking-fairness-in","title":"One Size Fits None: Rethinking Fairness in Medical AI","date":"2025-06-17","arxiv_id":"2506.14400","repositories_listed":0,"syntology":null},{"url":null,"slug":"equitable-electronic-health-record-prediction","title":"Equitable Electronic Health Record Prediction with FAME: Fairness-Aware Multimodal Embedding","date":"2025-06-16","arxiv_id":"2506.13104","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-optimality-for-demographic-parity","title":"Meta Optimality for Demographic Parity Constrained Regression via Post-Processing","date":"2025-06-16","arxiv_id":"2506.13947","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-research-for-machine-learning-should","title":"Fairness Research For Machine Learning Should Integrate Societal Considerations","date":"2025-06-14","arxiv_id":"2506.12556","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-fairness-assessment-of-dutch-hate","title":"Towards Fairness Assessment of Dutch Hate Speech Detection","date":"2025-06-14","arxiv_id":"2506.12502","repositories_listed":0,"syntology":null},{"url":null,"slug":"bias-amplification-in-rag-poisoning-knowledge","title":"Bias Amplification in RAG: Poisoning Knowledge Retrieval to Steer LLMs","date":"2025-06-13","arxiv_id":"2506.11415","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-the-effectiveness-of-deep-features","title":"Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis","date":"2025-06-13","arxiv_id":"2506.11753","repositories_listed":0,"syntology":null},{"url":null,"slug":"balancing-tails-when-comparing-distributions","title":"Balancing Tails when Comparing Distributions: Comprehensive Equity Index (CEI) with Application to Bias Evaluation in Operational Face Biometrics","date":"2025-06-12","arxiv_id":"2506.10564","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairasr-fair-audio-contrastive-learning-for","title":"FairASR: Fair Audio Contrastive Learning for Automatic Speech Recognition","date":"2025-06-12","arxiv_id":"2506.10747","repositories_listed":0,"syntology":null},{"url":null,"slug":"preserving-task-relevant-information-under","title":"Preserving Task-Relevant Information Under Linear Concept Removal","date":"2025-06-12","arxiv_id":"2506.10703","repositories_listed":0,"syntology":null},{"url":null,"slug":"surface-fairness-deep-bias-a-comparative","title":"Surface Fairness, Deep Bias: A Comparative Study of Bias in Language Models","date":"2025-06-12","arxiv_id":"2506.10491","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-10120","title":"GRAIL: A Benchmark for GRaph ActIve Learning in Dynamic Sensing Environments","date":"2025-06-11","arxiv_id":"2506.10120","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-responsible-ai-advances-in-safety","title":"Towards Responsible AI: Advances in Safety, Fairness, and Accountability of Autonomous Systems","date":"2025-06-11","arxiv_id":"2506.10192","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-08673","title":"Towards Fair Representation: Clustering and Consensus","date":"2025-06-10","arxiv_id":"2506.08673","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-08738","title":"Societal AI Research Has Become Less Interdisciplinary","date":"2025-06-10","arxiv_id":"2506.08738","repositories_listed":0,"syntology":null},{"url":null,"slug":"convergence-of-spectral-principal-paths-how","title":"Convergence of Spectral Principal Paths: How Deep Networks Distill Linear Representations from Noisy Inputs","date":"2025-06-10","arxiv_id":"2506.08543","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-is-not-silence-unmasking-vacuous","title":"Fairness is Not Silence: Unmasking Vacuous Neutrality in Small Language Models","date":"2025-06-10","arxiv_id":"2506.08487","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairtopia-envisioning-multi-agent","title":"FAIRTOPIA: Envisioning Multi-Agent Guardianship for Disrupting Unfair AI Pipelines","date":"2025-06-10","arxiv_id":"2506.09107","repositories_listed":0,"syntology":null},{"url":null,"slug":"hateful-person-or-hateful-model-investigating","title":"Hateful Person or Hateful Model? Investigating the Role of Personas in Hate Speech Detection by Large Language Models","date":"2025-06-10","arxiv_id":"2506.08593","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-08231","title":"Ensuring Reliability of Curated EHR-Derived Data: The Validation of Accuracy for LLM/ML-Extracted Information and Data (VALID) Framework","date":"2025-06-09","arxiv_id":"2506.08231","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-08263","title":"Learning-Based Multiuser Scheduling in MIMO-OFDM Systems with Hybrid Beamforming","date":"2025-06-09","arxiv_id":"2506.08263","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerating-spectral-clustering-under","title":"Accelerating Spectral Clustering under Fairness Constraints","date":"2025-06-09","arxiv_id":"2506.08143","repositories_listed":0,"syntology":null},{"url":null,"slug":"design-and-evaluation-of-deep-learning-based","title":"Design and Evaluation of Deep Learning-Based Dual-Spectrum Image Fusion Methods","date":"2025-06-09","arxiv_id":"2506.07779","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairdice-fairness-driven-offline-multi","title":"FairDICE: Fairness-Driven Offline Multi-Objective Reinforcement Learning","date":"2025-06-09","arxiv_id":"2506.08062","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-overfitting-in-machine-learning-an","title":"Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective","date":"2025-06-09","arxiv_id":"2506.07861","repositories_listed":0,"syntology":null},{"url":null,"slug":"hidden-bias-in-the-machine-stereotypes-in","title":"Hidden Bias in the Machine: Stereotypes in Text-to-Image Models","date":"2025-06-09","arxiv_id":"2506.13780","repositories_listed":0,"syntology":null},{"url":null,"slug":"statistical-hypothesis-testing-for-auditing","title":"Statistical Hypothesis Testing for Auditing Robustness in Language Models","date":"2025-06-09","arxiv_id":"2506.07947","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-08041","title":"The World of AI: A Novel Approach to AI Literacy for First-year Engineering Students","date":"2025-06-06","arxiv_id":"2506.08041","repositories_listed":0,"syntology":null},{"url":null,"slug":"co-vada-a-confidence-oriented-voice","title":"CO-VADA: A Confidence-Oriented Voice Augmentation Debiasing Approach for Fair Speech Emotion Recognition","date":"2025-06-06","arxiv_id":"2506.06071","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairmetrics-an-r-package-for-group-fairness","title":"fairmetrics: An R package for group fairness evaluation","date":"2025-06-06","arxiv_id":"2506.06243","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-04652","title":"EMO-Debias: Benchmarking Gender Debiasing Techniques in Multi-Label Speech Emotion Recognition","date":"2025-06-05","arxiv_id":"2506.04652","repositories_listed":0,"syntology":null},{"url":null,"slug":"fg-2025-trustfaa-the-first-workshop-on","title":"FG 2025 TrustFAA: the First Workshop on Towards Trustworthy Facial Affect Analysis: Advancing Insights of Fairness, Explainability, and Safety (TrustFAA)","date":"2025-06-05","arxiv_id":"2506.05095","repositories_listed":0,"syntology":null},{"url":null,"slug":"spectral-efficiency-maximization-for-mmwave","title":"Spectral Efficiency Maximization for mmWave MIMO-Aided Integrated Sensing and Communication Under Practical Constraints","date":"2025-06-05","arxiv_id":"2506.04683","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-agent-behavioral-science","title":"AI Agent Behavioral Science","date":"2025-06-04","arxiv_id":"2506.06366","repositories_listed":0,"syntology":null},{"url":null,"slug":"claim-an-intent-driven-multi-agent-framework","title":"CLAIM: An Intent-Driven Multi-Agent Framework for Analyzing Manipulation in Courtroom Dialogues","date":"2025-06-04","arxiv_id":"2506.04131","repositories_listed":0,"syntology":null},{"url":null,"slug":"crowd-sft-crowdsourcing-for-llm-alignment","title":"Crowd-SFT: Crowdsourcing for LLM Alignment","date":"2025-06-04","arxiv_id":"2506.04063","repositories_listed":0,"syntology":null},{"url":"/paper/fedfact-a-provable-framework-for-controllable","slug":"fedfact-a-provable-framework-for-controllable","title":"FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning","date":"2025-06-04","arxiv_id":"2506.03777","repositories_listed":0,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/fedfact-a-provable-framework-for-controllable#ran","syntology_url":"https://syntology.ai/paper/2506.03777","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.03777"}},"official":null}},{"url":null,"slug":"joint-beamforming-and-resource-allocation-for","title":"Beamforming and Resource Allocation for Delay Optimization in RIS-Assisted OFDM Systems","date":"2025-06-04","arxiv_id":"2506.03586","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-fair-and-effective-points-based","title":"Learning Fair And Effective Points-Based Rewards Programs","date":"2025-06-04","arxiv_id":"2506.03911","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-fairness-isn-t-statistical-the-limits-of","title":"When Fairness Isn't Statistical: The Limits of Machine Learning in Evaluating Legal Reasoning","date":"2025-06-04","arxiv_id":"2506.03913","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-pre-trained-framework-for-multilingual","title":"A Pre-trained Framework for Multilingual Brain Decoding Using Non-invasive Recordings","date":"2025-06-03","arxiv_id":"2506.03214","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-convergence-privacy-and-fairness","title":"Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling","date":"2025-06-03","arxiv_id":"2506.02422","repositories_listed":0,"syntology":null},{"url":null,"slug":"overcoming-challenges-of-partial-client","title":"Overcoming Challenges of Partial Client Participation in Federated Learning : A Comprehensive Review","date":"2025-06-03","arxiv_id":"2506.02887","repositories_listed":0,"syntology":null},{"url":null,"slug":"refined-metrics-sensing-limits-and-resource","title":"Refined Metrics, Sensing Limits, and Resource Allocation in OTFS-RSMA LEO ISAC","date":"2025-06-03","arxiv_id":"2506.02624","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-data-development-a-scorecard-for-the","title":"AI Data Development: A Scorecard for the System Card Framework","date":"2025-06-02","arxiv_id":"2506.02071","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-we-trust-machine-learning-the-reliability","title":"Can We Trust Machine Learning? The Reliability of Features from Open-Source Speech Analysis Tools for Speech Modeling","date":"2025-06-02","arxiv_id":"2506.11072","repositories_listed":0,"syntology":null},{"url":null,"slug":"selecting-for-less-discriminatory-algorithms","title":"Selecting for Less Discriminatory Algorithms: A Relational Search Framework for Navigating Fairness-Accuracy Trade-offs in Practice","date":"2025-06-02","arxiv_id":"2506.01594","repositories_listed":0,"syntology":null},{"url":null,"slug":"stochastically-dominant-peer-prediction","title":"Stochastically Dominant Peer Prediction","date":"2025-06-02","arxiv_id":"2506.02259","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-reinforcement-learning-approach-for-ris","title":"A Reinforcement Learning Approach for RIS-aided Fair Communications","date":"2025-06-01","arxiv_id":"2506.06344","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-disparate-effects-of-partial-information","title":"The Disparate Effects of Partial Information in Bayesian Strategic Learning","date":"2025-05-31","arxiv_id":"2506.00627","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-note-on-the-diversity-owen-values","title":"A note on the Diversity Owen values","date":"2025-05-30","arxiv_id":"2505.24171","repositories_listed":0,"syntology":null},{"url":null,"slug":"balancing-profit-and-fairness-in-risk-based","title":"Balancing Profit and Fairness in Risk-Based Pricing Markets","date":"2025-05-30","arxiv_id":"2506.00140","repositories_listed":0,"syntology":null}],"record_sha256":"883620448ea1ae746147b084107c9ad660f1507a88adcff67916827aa4f2234e","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}