{"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/54","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":54,"pages_in_order":57,"rows_per_page":100,"rows":[5301,5400],"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/53","next":"/task/fairness/papers/55","papers":[{"url":null,"slug":"mitigate-bias-in-face-recognition-using","title":"Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning","date":"2019-11-25","arxiv_id":"1911.10692","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-legal-compatibility-of-fairness","title":"On the Legal Compatibility of Fairness Definitions","date":"2019-11-25","arxiv_id":"1912.00761","repositories_listed":0,"syntology":null},{"url":null,"slug":"algorithmic-bias-in-recidivism-prediction-a","title":"Algorithmic Bias in Recidivism Prediction: A Causal Perspective","date":"2019-11-24","arxiv_id":"1911.10640","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-multi-party-machine-learning-a-game","title":"Fair Multi-party Machine Learning -- a Game Theoretic approach","date":"2019-11-22","arxiv_id":"1911.11555","repositories_listed":0,"syntology":null},{"url":null,"slug":"max-min-fair-precoder-design-and-power","title":"Max-Min Fair Precoder Design and Power Allocation for MU-MIMO NOMA","date":"2019-11-21","arxiv_id":"1911.09402","repositories_listed":0,"syntology":null},{"url":null,"slug":"bounded-temporal-fairness-for-fifo-financial","title":"Bounded Temporal Fairness for FIFO Financial Markets","date":"2019-11-20","arxiv_id":"1911.09209","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-human-body-is-a-black-box-supporting","title":"\"The Human Body is a Black Box\": Supporting Clinical Decision-Making with Deep Learning","date":"2019-11-19","arxiv_id":"1911.08089","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-with-minimal-harm-a-pareto-optimal","title":"Fairness With Minimal Harm: A Pareto-Optimal Approach For Healthcare","date":"2019-11-16","arxiv_id":"1911.06935","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-modeling-and-equilibria-in-fair","title":"Dynamic Modeling and Equilibria in Fair Decision Making","date":"2019-11-15","arxiv_id":"1911.06837","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-data-adaptation-with-quantile","title":"Fair Data Adaptation with Quantile Preservation","date":"2019-11-15","arxiv_id":"1911.06685","repositories_listed":0,"syntology":null},{"url":null,"slug":"buffer-aware-wireless-scheduling-based-on","title":"Buffer-aware Wireless Scheduling based on Deep Reinforcement Learning","date":"2019-11-13","arxiv_id":"1911.05281","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-fair-principal-component-analysis","title":"Efficient Fair Principal Component Analysis","date":"2019-11-12","arxiv_id":"1911.04931","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-intelligence-at-the-edge-with","title":"Machine Intelligence at the Edge with Learning Centric Power Allocation","date":"2019-11-12","arxiv_id":"1911.04922","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-through-equality-of-effort","title":"Fairness through Equality of Effort","date":"2019-11-11","arxiv_id":"1911.08292","repositories_listed":0,"syntology":null},{"url":null,"slug":"kernel-dependence-regularizers-and-gaussian","title":"Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness","date":"2019-11-11","arxiv_id":"1911.04322","repositories_listed":0,"syntology":null},{"url":null,"slug":"making-good-on-lstms-unfulfilled-promise","title":"Making Good on LSTMs' Unfulfilled Promise","date":"2019-11-11","arxiv_id":"1911.04489","repositories_listed":0,"syntology":null},{"url":null,"slug":"analyzing-bias-in-sensitive-personal","title":"Analyzing Bias in Sensitive Personal Information Used to Train Financial Models","date":"2019-11-09","arxiv_id":"1911.03623","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-human-in-the-loop-framework-to-construct","title":"A Human-in-the-loop Framework to Construct Context-aware Mathematical Notions of Outcome Fairness","date":"2019-11-08","arxiv_id":"1911.03020","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-introduction-to-artificial-intelligence","title":"An Introduction to Artificial Intelligence and Solutions to the Problems of Algorithmic Discrimination","date":"2019-11-08","arxiv_id":"1911.05755","repositories_listed":0,"syntology":null},{"url":null,"slug":"reducing-sentiment-bias-in-language-models-1","title":"Reducing Sentiment Bias in Language Models via Counterfactual Evaluation","date":"2019-11-08","arxiv_id":"1911.03064","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-state-of-nlp-literature-a-diachronic","title":"The State of NLP Literature: A Diachronic Analysis of the ACL Anthology","date":"2019-11-08","arxiv_id":"1911.03562","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-meta-learning-learning-how-to-learn","title":"Fair Meta-Learning: Learning How to Learn Fairly","date":"2019-11-06","arxiv_id":"1911.04336","repositories_listed":0,"syntology":null},{"url":null,"slug":"practical-compositional-fairness","title":"Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems","date":"2019-11-05","arxiv_id":"1911.01916","repositories_listed":0,"syntology":null},{"url":null,"slug":"auditing-and-achieving-intersectional","title":"Auditing and Achieving Intersectional Fairness in Classification Problems","date":"2019-11-04","arxiv_id":"1911.01468","repositories_listed":0,"syntology":null},{"url":null,"slug":"posing-fair-generalization-tasks-for-natural-1","title":"Posing Fair Generalization Tasks for Natural Language Inference","date":"2019-11-03","arxiv_id":"1911.00811","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-relationship-between-the-consistency-of","title":"The Relationship between the Consistency of Users' Ratings and Recommendation Calibration","date":"2019-11-03","arxiv_id":"1911.00852","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-predictors-under-distribution-shift","title":"Fairness Violations and Mitigation under Covariate Shift","date":"2019-11-02","arxiv_id":"1911.00677","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-treatment-allocations-in-social-networks","title":"Fair treatment allocations in social networks","date":"2019-11-01","arxiv_id":"1911.05489","repositories_listed":0,"syntology":null},{"url":null,"slug":"methodological-blind-spots-in-machine","title":"Methodological Blind Spots in Machine Learning Fairness: Lessons from the Philosophy of Science and Computer Science","date":"2019-10-31","arxiv_id":"1910.14210","repositories_listed":0,"syntology":null},{"url":null,"slug":"dadi-dynamic-discovery-of-fair-information","title":"DADI: Dynamic Discovery of Fair Information with Adversarial Reinforcement Learning","date":"2019-10-30","arxiv_id":"1910.13983","repositories_listed":0,"syntology":null},{"url":null,"slug":"fault-tolerance-of-neural-networks-in","title":"Fault Tolerance of Neural Networks in Adversarial Settings","date":"2019-10-30","arxiv_id":"1910.13875","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-is-fair-exploring-pareto-efficiency-for","title":"What is Fair? Exploring Pareto-Efficiency for Fairness Constrained Classifiers","date":"2019-10-30","arxiv_id":"1910.14120","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-a-better-trade-off-between-performance","title":"Toward a better trade-off between performance and fairness with kernel-based distribution matching","date":"2019-10-25","arxiv_id":"1910.11779","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-sample-complexity-and-the-case-for","title":"Fairness Sample Complexity and the Case for Human Intervention","date":"2019-10-24","arxiv_id":"1910.11452","repositories_listed":0,"syntology":null},{"url":null,"slug":"pc-fairness-a-unified-framework-for-measuring","title":"PC-Fairness: A Unified Framework for Measuring Causality-based Fairness","date":"2019-10-20","arxiv_id":"1910.12586","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-information-theoretic-perspective-on-the","title":"Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing","date":"2019-10-17","arxiv_id":"1910.07870","repositories_listed":0,"syntology":null},{"url":null,"slug":"max-min-fairness-of-k-user-cooperative-rate","title":"Max-min Fairness of K-user Cooperative Rate-Splitting in MISO Broadcast Channel with User Relaying","date":"2019-10-17","arxiv_id":"1910.07843","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-impact-of-popularity-bias-on-fairness-and","title":"The Impact of Popularity Bias on Fairness and Calibration in Recommendation","date":"2019-10-13","arxiv_id":"1910.05755","repositories_listed":0,"syntology":null},{"url":null,"slug":"measuring-unfairness-through-game-theoretic","title":"Measuring Unfairness through Game-Theoretic Interpretability","date":"2019-10-12","arxiv_id":"1910.05591","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-in-clustering-with-multiple","title":"Fairness in Clustering with Multiple Sensitive Attributes","date":"2019-10-11","arxiv_id":"1910.05113","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-case-for-evaluating-causal-models-using","title":"The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data","date":"2019-10-11","arxiv_id":"1910.05387","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-for-explaining-decisions-in-multi-agent","title":"AI for Explaining Decisions in Multi-Agent Environments","date":"2019-10-10","arxiv_id":"1910.04404","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-training-of-fair-predictive-models","title":"Optimal Training of Fair Predictive Models","date":"2019-10-09","arxiv_id":"1910.04109","repositories_listed":0,"syntology":null},{"url":"/paper/refuge-challenge-a-unified-framework-for","slug":"refuge-challenge-a-unified-framework-for","title":"REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs","date":"2019-10-08","arxiv_id":"1910.03667","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-impact-of-data-preparation-on-the","title":"The Impact of Data Preparation on the Fairness of Software Systems","date":"2019-10-05","arxiv_id":"1910.02321","repositories_listed":0,"syntology":null},{"url":null,"slug":"group-based-fair-learning-leads-to-counter","title":"Group-based Fair Learning Leads to Counter-intuitive Predictions","date":"2019-10-04","arxiv_id":"1910.02097","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-aware-text-rewriting","title":"Privacy-Aware Text Rewriting","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-generative-adversarial","title":"Generating Fair Universal Representations using Adversarial Models","date":"2019-09-27","arxiv_id":"1910.00411","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-approach-to-fair-distribution-of","title":"A New Approach to Fair Distribution of Welfare","date":"2019-09-25","arxiv_id":"1909.11346","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-with-wasserstein-adversarial","title":"Fairness with Wasserstein Adversarial Networks","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fr-gan-fair-and-robust-training","title":"FR-GAN: Fair and Robust Training","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"geometry-aware-generation-of-adversarial-and","title":"Geometry-aware Generation of Adversarial and Cooperative Point Clouds","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pareto-optimality-in-no-harm-fairness","title":"Pareto Optimality in No-Harm Fairness","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"stablizing-adversarial-invariance-induction","title":"Stablizing Adversarial Invariance Induction by Discriminator Matching","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-fairness-accuracy-landscape-of-neural","title":"The fairness-accuracy landscape of neural classifiers","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"power-allocation-in-cache-aided-noma-systems","title":"Power Allocation in Cache-Aided NOMA Systems: Optimization and Deep Reinforcement Learning Approaches","date":"2019-09-24","arxiv_id":"1909.11074","repositories_listed":0,"syntology":null},{"url":null,"slug":"190910502","title":"Weighted Envy-Freeness in Indivisible Item Allocation","date":"2019-09-23","arxiv_id":"1909.10502","repositories_listed":0,"syntology":null},{"url":null,"slug":"advancing-subgroup-fairness-via-sleeping","title":"Advancing subgroup fairness via sleeping experts","date":"2019-09-18","arxiv_id":"1909.08375","repositories_listed":0,"syntology":null},{"url":null,"slug":"truthful-and-faithful-monetary-policy-for-a","title":"Truthful and Faithful Monetary Policy for a Stablecoin Conducted by a Decentralised, Encrypted Artificial Intelligence","date":"2019-09-16","arxiv_id":"1909.07445","repositories_listed":0,"syntology":null},{"url":null,"slug":"addressing-algorithmic-bottlenecks-in-elastic","title":"Addressing Algorithmic Bottlenecks in Elastic Machine Learning with Chicle","date":"2019-09-11","arxiv_id":"1909.04885","repositories_listed":0,"syntology":null},{"url":null,"slug":"q-learning-based-aerial-base-station","title":"Q-Learning Based Aerial Base Station Placement for Fairness Enhancement in Mobile Networks","date":"2019-09-10","arxiv_id":"1909.08093","repositories_listed":0,"syntology":null},{"url":null,"slug":"equalizing-recourse-across-groups","title":"Equalizing Recourse across Groups","date":"2019-09-07","arxiv_id":"1909.03166","repositories_listed":0,"syntology":null},{"url":null,"slug":"pretrained-ai-models-performativity-mobility","title":"Pretrained AI Models: Performativity, Mobility, and Change","date":"2019-09-07","arxiv_id":"1909.03290","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-reinforcement-learning-based-approach-for","title":"Reinforcement Learning for Joint Optimization of Multiple Rewards","date":"2019-09-06","arxiv_id":"1909.02940","repositories_listed":0,"syntology":null},{"url":null,"slug":"approaching-machine-learning-fairness-through","title":"Approaching Machine Learning Fairness through Adversarial Network","date":"2019-09-06","arxiv_id":"1909.03013","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-generalized-rate-metrics-through","title":"Optimizing Generalized Rate Metrics through Game Equilibrium","date":"2019-09-06","arxiv_id":"1909.02939","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantifying-infra-marginality-and-its-trade","title":"Quantifying Infra-Marginality and Its Trade-off with Group Fairness","date":"2019-09-03","arxiv_id":"1909.00982","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-aware-process-mining","title":"Fairness-Aware Process Mining","date":"2019-08-28","arxiv_id":"1908.11451","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-objective-automatic-machine-learning","title":"Multi-Objective Automatic Machine Learning with AutoxgboostMC","date":"2019-08-28","arxiv_id":"1908.10796","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-center-in-your-neighborhood-fairness-in","title":"A Center in Your Neighborhood: Fairness in Facility Location","date":"2019-08-23","arxiv_id":"1908.09041","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-in-deep-learning-a-computational","title":"Fairness in Deep Learning: A Computational Perspective","date":"2019-08-23","arxiv_id":"1908.08843","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-management-for-causal-algorithmic","title":"Data Management for Causal Algorithmic Fairness","date":"2019-08-20","arxiv_id":"1908.07924","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-fair-classifiers-in-online","title":"Towards Reducing Biases in Combining Multiple Experts Online","date":"2019-08-19","arxiv_id":"1908.07009","repositories_listed":0,"syntology":null},{"url":null,"slug":"recommender-systems-fairness-evaluation-via","title":"Recommender Systems Fairness Evaluation via Generalized Cross Entropy","date":"2019-08-19","arxiv_id":"1908.06708","repositories_listed":0,"syntology":null},{"url":null,"slug":"radio-resource-management-for-v2v-multihop","title":"Radio Resource Management for V2V Multihop Communication Considering Adjacent Channel Interference","date":"2019-08-12","arxiv_id":"1908.06866","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-inference-for-large-scale-image","title":"Bayesian Inference for Large Scale Image Classification","date":"2019-08-09","arxiv_id":"1908.03491","repositories_listed":0,"syntology":null},{"url":null,"slug":"conservatives-overfit-liberals-underfit-the","title":"\"Conservatives Overfit, Liberals Underfit\": The Social-Psychological Control of Affect and Uncertainty","date":"2019-08-08","arxiv_id":"1908.03106","repositories_listed":0,"syntology":null},{"url":null,"slug":"paired-consistency-an-example-based-model","title":"Paired-Consistency: An Example-Based Model-Agnostic Approach to Fairness Regularization in Machine Learning","date":"2019-08-07","arxiv_id":"1908.02641","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-study-in-rashomon-curves-and-volumes-a-new","title":"On the Existence of Simpler Machine Learning Models","date":"2019-08-05","arxiv_id":"1908.01755","repositories_listed":0,"syntology":null},{"url":null,"slug":"discovery-of-bias-and-strategic-behavior-in","title":"Discovery of Bias and Strategic Behavior in Crowdsourced Performance Assessment","date":"2019-08-05","arxiv_id":"1908.01718","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-is-the-point-of-fairness-disability-ai","title":"What is the Point of Fairness? Disability, AI and The Complexity of Justice","date":"2019-08-02","arxiv_id":"1908.01024","repositories_listed":0,"syntology":null},{"url":null,"slug":"equalizing-gender-bias-in-neural-machine","title":"Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"equity-beyond-bias-in-language-technologies","title":"Equity Beyond Bias in Language Technologies for Education","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-many-dimensions-of-algorithmic-fairness","title":"The many dimensions of algorithmic fairness in educational applications","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"adapting-square-for-quality-assessment-of","title":"Adapting SQuaRE for Quality Assessment of Artificial Intelligence Systems","date":"2019-07-31","arxiv_id":"1908.02134","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-stakeholder-recommendation-and-its","title":"Multi-stakeholder Recommendation and its Connection to Multi-sided Fairness","date":"2019-07-30","arxiv_id":"1907.13158","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-model-accuracy-and-explanation-fidelity","title":"How model accuracy and explanation fidelity influence user trust","date":"2019-07-26","arxiv_id":"1907.12652","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-in-reinforcement-learning-2","title":"Fairness in Reinforcement Learning","date":"2019-07-24","arxiv_id":"1907.10323","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-logical-specification-of-statistical","title":"Towards Logical Specification of Statistical Machine Learning","date":"2019-07-24","arxiv_id":"1907.10327","repositories_listed":0,"syntology":null},{"url":null,"slug":"achieving-fairness-in-the-stochastic-multi","title":"Achieving Fairness in the Stochastic Multi-armed Bandit Problem","date":"2019-07-23","arxiv_id":"1907.10516","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-conceptual-framework-for-evaluating","title":"A Conceptual Framework for Evaluating Fairness in Search","date":"2019-07-22","arxiv_id":"1907.09328","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparative-evaluation-of-multiagent-learning","title":"Comparative Evaluation of Multiagent Learning Algorithms in a Diverse Set of Ad Hoc Team Problems","date":"2019-07-22","arxiv_id":"1907.09189","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-quantile-regression","title":"Fair quantile regression","date":"2019-07-19","arxiv_id":"1907.08646","repositories_listed":0,"syntology":null},{"url":null,"slug":"cads-core-aware-dynamic-scheduler-for","title":"CADS: Core-Aware Dynamic Scheduler for Multicore Memory Controllers","date":"2019-07-17","arxiv_id":"1907.07776","repositories_listed":0,"syntology":null},{"url":null,"slug":"almost-group-envy-free-allocation-of","title":"Almost Group Envy-free Allocation of Indivisible Goods and Chores","date":"2019-07-16","arxiv_id":"1907.09279","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-and-diversity-in-the-recommendation","title":"Fairness and Diversity in the Recommendation and Ranking of Participatory Media Content","date":"2019-07-16","arxiv_id":"1907.07253","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-enhancing-interventions-in-stream","title":"Fairness-enhancing interventions in stream classification","date":"2019-07-16","arxiv_id":"1907.07223","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-causal-bayesian-networks-viewpoint-on","title":"A Causal Bayesian Networks Viewpoint on Fairness","date":"2019-07-15","arxiv_id":"1907.06430","repositories_listed":0,"syntology":null},{"url":null,"slug":"counterfactual-reasoning-for-fair-clinical","title":"Counterfactual Reasoning for Fair Clinical Risk Prediction","date":"2019-07-14","arxiv_id":"1907.06260","repositories_listed":0,"syntology":null},{"url":null,"slug":"proceedings-of-facts-ir-2019","title":"Proceedings of FACTS-IR 2019","date":"2019-07-12","arxiv_id":"1907.05755","repositories_listed":0,"syntology":null}],"record_sha256":"d212fab97d0c1e992e489f010233e72dd4fa8096104adb5cf63611784e6805ea","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}