{"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/36","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":36,"pages_in_order":57,"rows_per_page":100,"rows":[3501,3600],"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/35","next":"/task/fairness/papers/37","papers":[{"url":null,"slug":"the-flawed-foundations-of-fair-machine","title":"The Flawed Foundations of Fair Machine Learning","date":"2023-06-02","arxiv_id":"2306.01417","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-fairness-aware-recommender","title":"A Survey on Fairness-aware Recommender Systems","date":"2023-06-01","arxiv_id":"2306.00403","repositories_listed":0,"syntology":null},{"url":null,"slug":"achieving-fairness-in-multi-agent-markov","title":"Achieving Fairness in Multi-Agent Markov Decision Processes Using Reinforcement Learning","date":"2023-06-01","arxiv_id":"2306.00324","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-survey-taxonomy-and-future-directions-of","title":"Survey of Trustworthy AI: A Meta Decision of AI","date":"2023-06-01","arxiv_id":"2306.00380","repositories_listed":0,"syntology":null},{"url":null,"slug":"cooperative-iot-data-sharing-with","title":"Cooperative IoT Data Sharing with Heterogeneity of Participants Based on Electricity Retail","date":"2023-05-31","arxiv_id":"2305.20024","repositories_listed":0,"syntology":null},{"url":null,"slug":"doubly-constrained-fair-clustering","title":"Doubly Constrained Fair Clustering","date":"2023-05-31","arxiv_id":"2305.19475","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-fair-disentangled-online-learning-for","title":"Towards Fair Disentangled Online Learning for Changing Environments","date":"2023-05-31","arxiv_id":"2306.01007","repositories_listed":0,"syntology":null},{"url":null,"slug":"adapting-fairness-interventions-to-missing","title":"Adapting Fairness Interventions to Missing Values","date":"2023-05-30","arxiv_id":"2305.19429","repositories_listed":0,"syntology":null},{"url":null,"slug":"disentangling-and-operationalizing-ai","title":"Disentangling and Operationalizing AI Fairness at LinkedIn","date":"2023-05-30","arxiv_id":"2306.00025","repositories_listed":0,"syntology":null},{"url":null,"slug":"examining-risks-of-racial-biases-in-nlp-tools","title":"Examining risks of racial biases in NLP tools for child protective services","date":"2023-05-30","arxiv_id":"2305.19409","repositories_listed":0,"syntology":null},{"url":null,"slug":"framm-fair-ranking-with-missing-modalities","title":"FRAMM: Fair Ranking with Missing Modalities for Clinical Trial Site Selection","date":"2023-05-30","arxiv_id":"2305.19407","repositories_listed":0,"syntology":null},{"url":null,"slug":"pointwise-representational-similarity","title":"Pointwise Representational Similarity","date":"2023-05-30","arxiv_id":"2305.19294","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-alternating-time-m-calculus-with","title":"The Alternating-Time μ-Calculus With Disjunctive Explicit Strategies","date":"2023-05-30","arxiv_id":"2305.18795","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-leximin-approach-for-a-sequence-of","title":"The Leximin Approach for a Sequence of Collective Decisions","date":"2023-05-29","arxiv_id":"2305.18024","repositories_listed":0,"syntology":null},{"url":null,"slug":"monotonicity-anomalies-in-scottish-local","title":"Monotonicity Anomalies in Scottish Local Government Elections","date":"2023-05-28","arxiv_id":"2305.17741","repositories_listed":0,"syntology":null},{"url":null,"slug":"predictability-and-fairness-in-load-1","title":"Predictability and Fairness in Load Aggregation with Deadband","date":"2023-05-28","arxiv_id":"2305.17725","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-clustering-via-hierarchical-fair","title":"Fair Clustering via Hierarchical Fair-Dirichlet Process","date":"2023-05-27","arxiv_id":"2305.17557","repositories_listed":0,"syntology":null},{"url":null,"slug":"moral-machine-or-tyranny-of-the-majority","title":"Moral Machine or Tyranny of the Majority?","date":"2023-05-27","arxiv_id":"2305.17319","repositories_listed":0,"syntology":null},{"url":null,"slug":"fara-future-aware-ranking-algorithm-for","title":"FARA: Future-aware Ranking Algorithm for Fairness Optimization","date":"2023-05-26","arxiv_id":"2305.16637","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-certification-of-machine-learning","title":"Rethinking Certification for Trustworthy Machine Learning-Based Applications","date":"2023-05-26","arxiv_id":"2305.16822","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairdp-certified-fairness-with-differential","title":"FairDP: Certified Fairness with Differential Privacy","date":"2023-05-25","arxiv_id":"2305.16474","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-continual-learning-approach-to","title":"Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments","date":"2023-05-25","arxiv_id":"2305.15700","repositories_listed":0,"syntology":null},{"url":null,"slug":"gfairhint-improving-individual-fairness-for","title":"GFairHint: Improving Individual Fairness for Graph Neural Networks via Fairness Hint","date":"2023-05-25","arxiv_id":"2305.15622","repositories_listed":0,"syntology":null},{"url":null,"slug":"monitoring-algorithmic-fairness","title":"Monitoring Algorithmic Fairness","date":"2023-05-25","arxiv_id":"2305.15979","repositories_listed":0,"syntology":null},{"url":null,"slug":"small-total-cost-constraints-in-contextual","title":"Small Total-Cost Constraints in Contextual Bandits with Knapsacks, with Application to Fairness","date":"2023-05-25","arxiv_id":"2305.15807","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-trip-towards-fairness-bias-and-de-biasing","title":"A Trip Towards Fairness: Bias and De-Biasing in Large Language Models","date":"2023-05-23","arxiv_id":"2305.13862","repositories_listed":0,"syntology":null},{"url":null,"slug":"curse-of-low-dimensionality-in-recommender","title":"Curse of \"Low\" Dimensionality in Recommender Systems","date":"2023-05-23","arxiv_id":"2305.13597","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-all-languages-cost-the-same-tokenization","title":"Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models","date":"2023-05-23","arxiv_id":"2305.13707","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-differentially-private-federated","title":"Fair Differentially Private Federated Learning Framework","date":"2023-05-23","arxiv_id":"2305.13878","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-oversampling-technique-using","title":"Fair Oversampling Technique using Heterogeneous Clusters","date":"2023-05-23","arxiv_id":"2305.13875","repositories_listed":0,"syntology":null},{"url":null,"slug":"fitness-a-causal-de-correlation-approach-for","title":"FITNESS: A Causal De-correlation Approach for Mitigating Bias in Machine Learning Software","date":"2023-05-23","arxiv_id":"2305.14396","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-relevance-of-apis-facing-fairwashed","title":"Mitigating fairwashing using Two-Source Audits","date":"2023-05-23","arxiv_id":"2305.13883","repositories_listed":0,"syntology":null},{"url":null,"slug":"causality-aided-trade-off-analysis-for","title":"Causality-Aided Trade-off Analysis for Machine Learning Fairness","date":"2023-05-22","arxiv_id":"2305.13057","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-the-impact-of-social-determinants","title":"Evaluating the Impact of Social Determinants on Health Prediction in the Intensive Care Unit","date":"2023-05-22","arxiv_id":"2305.12622","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalizing-fairness-using-multi-task","title":"Transferring Fairness using Multi-Task Learning with Limited Demographic Information","date":"2023-05-22","arxiv_id":"2305.12671","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-bias-and-fairness-in-nlp-how-to-have-a","title":"On Bias and Fairness in NLP: Investigating the Impact of Bias and Debiasing in Language Models on the Fairness of Toxicity Detection","date":"2023-05-22","arxiv_id":"2305.12829","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-to-capture-intersectional-fairness","title":"Fair Without Leveling Down: A New Intersectional Fairness Definition","date":"2023-05-21","arxiv_id":"2305.12495","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-limitations-of-simulating-active","title":"On the Limitations of Simulating Active Learning","date":"2023-05-21","arxiv_id":"2305.13342","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-debiasing-via-gradient-based","title":"Model Debiasing via Gradient-based Explanation on Representation","date":"2023-05-20","arxiv_id":"2305.12178","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparing-software-developers-with-chatgpt-an","title":"Comparing Software Developers with ChatGPT: An Empirical Investigation","date":"2023-05-19","arxiv_id":"2305.11837","repositories_listed":0,"syntology":null},{"url":null,"slug":"latent-imitator-generating-natural-individual","title":"Latent Imitator: Generating Natural Individual Discriminatory Instances for Black-Box Fairness Testing","date":"2023-05-19","arxiv_id":"2305.11602","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-fairness-impacts-of-private-ensembles","title":"On the Fairness Impacts of Private Ensembles Models","date":"2023-05-19","arxiv_id":"2305.11807","repositories_listed":0,"syntology":null},{"url":null,"slug":"trustworthy-federated-learning-a-survey","title":"Trustworthy Federated Learning: A Survey","date":"2023-05-19","arxiv_id":"2305.11537","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparing-biases-and-the-impact-of","title":"Comparing Biases and the Impact of Multilingual Training across Multiple Languages","date":"2023-05-18","arxiv_id":"2305.11242","repositories_listed":0,"syntology":null},{"url":null,"slug":"prevention-is-better-than-cure-a-case-study","title":"Prevention is better than cure: a case study of the abnormalities detection in the chest","date":"2023-05-18","arxiv_id":"2305.10961","repositories_listed":0,"syntology":null},{"url":null,"slug":"i-m-fully-who-i-am-towards-centering","title":"\"I'm fully who I am\": Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation","date":"2023-05-17","arxiv_id":"2305.09941","repositories_listed":0,"syntology":null},{"url":null,"slug":"consensus-and-subjectivity-of-skin-tone","title":"Consensus and Subjectivity of Skin Tone Annotation for ML Fairness","date":"2023-05-16","arxiv_id":"2305.09073","repositories_listed":0,"syntology":null},{"url":null,"slug":"consumer-side-fairness-in-recommender-systems","title":"Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation","date":"2023-05-16","arxiv_id":"2305.09330","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-and-diversity-in-information-access","title":"Fairness and Diversity in Information Access Systems","date":"2023-05-16","arxiv_id":"2305.09319","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-s-the-problem-linda-the-conjunction","title":"What's the Problem, Linda? The Conjunction Fallacy as a Fairness Problem","date":"2023-05-16","arxiv_id":"2305.09535","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-based-qoe-aware-digital-twin","title":"Attention-based QoE-aware Digital Twin Empowered Edge Computing for Immersive Virtual Reality","date":"2023-05-15","arxiv_id":"2305.08569","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-bias-management","title":"Data Bias Management","date":"2023-05-15","arxiv_id":"2305.09686","repositories_listed":0,"syntology":null},{"url":null,"slug":"private-training-set-inspection-in-mlaas","title":"Private Training Set Inspection in MLaaS","date":"2023-05-15","arxiv_id":"2305.09058","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-prototype-implementation-of-rate-splitting","title":"Rate-Splitting Multiple Access: The First Prototype and Experimental Validation of its Superiority over SDMA and NOMA","date":"2023-05-12","arxiv_id":"2305.07361","repositories_listed":0,"syntology":null},{"url":null,"slug":"learn-to-unlearn-a-survey-on-machine","title":"Learn to Unlearn: A Survey on Machine Unlearning","date":"2023-05-12","arxiv_id":"2305.07512","repositories_listed":0,"syntology":null},{"url":null,"slug":"parameterized-approximation-for-robust","title":"Parameterized Approximation for Robust Clustering in Discrete Geometric Spaces","date":"2023-05-12","arxiv_id":"2305.07316","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-statistical-approach-to-detect-sensitive","title":"A statistical approach to detect sensitive features in a group fairness setting","date":"2023-05-11","arxiv_id":"2305.06994","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-intersectional-fairness-in","title":"A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challenges","date":"2023-05-11","arxiv_id":"2305.06969","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-in-machine-learning-meets-with","title":"Fairness in Machine Learning meets with Equity in Healthcare","date":"2023-05-11","arxiv_id":"2305.07041","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-the-landscape-of-machine-unlearning","title":"Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy","date":"2023-05-10","arxiv_id":"2305.06360","repositories_listed":0,"syntology":null},{"url":null,"slug":"gpt-agents-in-game-theory-experiments","title":"GPT in Game Theory Experiments","date":"2023-05-09","arxiv_id":"2305.05516","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-unraveling-calibration-biases-in","title":"Towards unraveling calibration biases in medical image analysis","date":"2023-05-09","arxiv_id":"2305.05101","repositories_listed":0,"syntology":null},{"url":null,"slug":"first-choice-maximality-meets-ex-ante-and-ex","title":"First-Choice Maximality Meets Ex-ante and Ex-post Fairness","date":"2023-05-08","arxiv_id":"2305.04589","repositories_listed":0,"syntology":null},{"url":null,"slug":"mo-dehb-evolutionary-based-hyperband-for","title":"MO-DEHB: Evolutionary-based Hyperband for Multi-Objective Optimization","date":"2023-05-08","arxiv_id":"2305.04502","repositories_listed":0,"syntology":null},{"url":null,"slug":"runtime-monitoring-of-dynamic-fairness","title":"Runtime Monitoring of Dynamic Fairness Properties","date":"2023-05-08","arxiv_id":"2305.04699","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-class-imbalance-in-machine","title":"Rethinking Class Imbalance in Machine Learning","date":"2023-05-06","arxiv_id":"2305.03900","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-study-on-dataset-distillation","title":"A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness","date":"2023-05-05","arxiv_id":"2305.03355","repositories_listed":0,"syntology":null},{"url":null,"slug":"algorithmic-unfairness-through-the-lens-of-eu","title":"Algorithmic Unfairness through the Lens of EU Non-Discrimination Law: Or Why the Law is not a Decision Tree","date":"2023-05-05","arxiv_id":"2305.13938","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-aided-beamforming-for-downlink","title":"Deep Learning Aided Beamforming for Downlink Non-Orthogonal Multiple Access Systems","date":"2023-05-04","arxiv_id":"2305.02744","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributing-synergy-functions-unifying-game","title":"Distributing Synergy Functions: Unifying Game-Theoretic Interaction Methods for Machine-Learning Explainability","date":"2023-05-04","arxiv_id":"2305.03100","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrating-psychometrics-and-computing","title":"Integrating Psychometrics and Computing Perspectives on Bias and Fairness in Affective Computing: A Case Study of Automated Video Interviews","date":"2023-05-04","arxiv_id":"2305.02629","repositories_listed":0,"syntology":null},{"url":null,"slug":"mlhops-machine-learning-for-healthcare","title":"MLHOps: Machine Learning for Healthcare Operations","date":"2023-05-04","arxiv_id":"2305.02474","repositories_listed":0,"syntology":null},{"url":null,"slug":"recent-advances-in-the-foundations-and","title":"Recent Advances in the Foundations and Applications of Unbiased Learning to Rank","date":"2023-05-04","arxiv_id":"2305.02914","repositories_listed":0,"syntology":null},{"url":null,"slug":"explaining-language-models-predictions-with","title":"Explaining Language Models' Predictions with High-Impact Concepts","date":"2023-05-03","arxiv_id":"2305.02160","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairml-a-statistician-s-take-on-fair-machine","title":"fairml: A Statistician's Take on Fair Machine Learning Modelling","date":"2023-05-03","arxiv_id":"2305.02009","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-in-ai-systems-mitigating-gender-bias","title":"Fairness in AI Systems: Mitigating gender bias from language-vision models","date":"2023-05-03","arxiv_id":"2305.01888","repositories_listed":0,"syntology":null},{"url":null,"slug":"are-demographically-invariant-models-and","title":"Are demographically invariant models and representations in medical imaging fair?","date":"2023-05-02","arxiv_id":"2305.01397","repositories_listed":0,"syntology":null},{"url":null,"slug":"connecting-the-dots-in-trustworthy-artificial","title":"Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation","date":"2023-05-02","arxiv_id":"2305.02231","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-and-representation-in-satellite","title":"Fairness and representation in satellite-based poverty maps: Evidence of urban-rural disparities and their impacts on downstream policy","date":"2023-05-02","arxiv_id":"2305.01783","repositories_listed":0,"syntology":null},{"url":null,"slug":"multidimensional-fairness-in-paper","title":"Multidimensional Fairness in Paper Recommendation","date":"2023-05-02","arxiv_id":"2305.01141","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-impact-of-data-quality-on-image","title":"On the Impact of Data Quality on Image Classification Fairness","date":"2023-05-02","arxiv_id":"2305.01595","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-re-rank-with-constrained-meta","title":"Learning to Re-rank with Constrained Meta-Optimal Transport","date":"2023-04-29","arxiv_id":"2305.00319","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-privacy-utility-and-efficiency-in","title":"Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learning","date":"2023-04-29","arxiv_id":"2305.00312","repositories_listed":0,"syntology":null},{"url":null,"slug":"visualizing-chest-x-ray-dataset-biases-using","title":"Visualizing chest X-ray dataset biases using GANs","date":"2023-04-29","arxiv_id":"2305.00147","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-federated-reinforcement-learning-framework","title":"A Federated Reinforcement Learning Framework for Link Activation in Multi-link Wi-Fi Networks","date":"2023-04-28","arxiv_id":"2304.14720","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-to-address-monotonicity-for-model-risk","title":"How to address monotonicity for model risk management?","date":"2023-04-28","arxiv_id":"2305.00799","repositories_listed":0,"syntology":null},{"url":null,"slug":"social-preferences-and-deliberately","title":"Social Preferences and Deliberately Stochastic Behavior","date":"2023-04-28","arxiv_id":"2304.14977","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-uncertainty-quantification-how","title":"Fairness Uncertainty Quantification: How certain are you that the model is fair?","date":"2023-04-27","arxiv_id":"2304.13950","repositories_listed":0,"syntology":null},{"url":null,"slug":"oversampling-higher-performing-minorities","title":"Oversampling Higher-Performing Minorities During Machine Learning Model Training Reduces Adverse Impact Slightly but Also Reduces Model Accuracy","date":"2023-04-27","arxiv_id":"2304.13933","repositories_listed":0,"syntology":null},{"url":null,"slug":"proportionally-representative-clustering","title":"Proportionally Representative Clustering","date":"2023-04-27","arxiv_id":"2304.13917","repositories_listed":0,"syntology":null},{"url":null,"slug":"why-not-both-complementing-explanations-with","title":"Why not both? Complementing explanations with uncertainty, and the role of self-confidence in Human-AI collaboration","date":"2023-04-27","arxiv_id":"2304.14130","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-clinical-ai-fairness-a-translational","title":"Towards clinical AI fairness: A translational perspective","date":"2023-04-26","arxiv_id":"2304.13493","repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-adversaries-with-anti-adversaries","title":"Combining Adversaries with Anti-adversaries in Training","date":"2023-04-25","arxiv_id":"2304.12550","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-and-bias-in-truth-discovery","title":"Fairness and Bias in Truth Discovery Algorithms: An Experimental Analysis","date":"2023-04-25","arxiv_id":"2304.12573","repositories_listed":0,"syntology":null},{"url":null,"slug":"artificial-general-intelligence-agi-for","title":"AGI: Artificial General Intelligence for Education","date":"2023-04-24","arxiv_id":"2304.12479","repositories_listed":0,"syntology":null},{"url":null,"slug":"excalibr-expected-calibration-of","title":"ExCalibR: Expected Calibration of Recommendations","date":"2023-04-24","arxiv_id":"2304.12311","repositories_listed":0,"syntology":null},{"url":null,"slug":"space-time-reconfigurable-intelligent","title":"Rapidly time-varying reconfigurable intelligent surfaces for downlink multiuser transmissions","date":"2023-04-24","arxiv_id":"2304.11912","repositories_listed":0,"syntology":null},{"url":null,"slug":"ensuring-trustworthy-medical-artificial","title":"A Conceptual Algorithm for Applying Ethical Principles of AI to Medical Practice","date":"2023-04-23","arxiv_id":"2304.11530","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-independence-of-association-bias-and","title":"On the Independence of Association Bias and Empirical Fairness in Language Models","date":"2023-04-20","arxiv_id":"2304.10153","repositories_listed":0,"syntology":null},{"url":null,"slug":"acrocpolis-a-descriptive-framework-for-making","title":"ACROCPoLis: A Descriptive Framework for Making Sense of Fairness","date":"2023-04-19","arxiv_id":"2304.11217","repositories_listed":0,"syntology":null}],"record_sha256":"a548ad61bd8c08493aec86b4eb5eef45964ec41caaf6debe3f863b7f2489bda6","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}