{"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/16","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":16,"pages_in_order":57,"rows_per_page":100,"rows":[1501,1600],"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/15","next":"/task/fairness/papers/17","papers":[{"url":"/paper/versatile-verification-of-tree-ensembles","slug":"versatile-verification-of-tree-ensembles","title":"Versatile Verification of Tree Ensembles","date":"2020-10-26","arxiv_id":"2010.13880","repositories_listed":1,"syntology":null},{"url":"/paper/fairput-a-light-framework-for-machine","slug":"fairput-a-light-framework-for-machine","title":"FairPut: A Light Framework for Machine Learning Fairness with LightGBM","date":"2020-10-22","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/the-pursuit-of-algorithmic-fairness-on","slug":"the-pursuit-of-algorithmic-fairness-on","title":"The Pursuit of Algorithmic Fairness: On \"Correcting\" Algorithmic Unfairness in a Child Welfare Reunification Success Classifier","date":"2020-10-22","arxiv_id":"2010.12089","repositories_listed":1,"syntology":null},{"url":"/paper/how-do-fair-decisions-fare-in-long-term","slug":"how-do-fair-decisions-fare-in-long-term","title":"How Do Fair Decisions Fare in Long-term Qualification?","date":"2020-10-21","arxiv_id":"2010.11300","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/how-do-fair-decisions-fare-in-long-term#ran","syntology_url":"https://syntology.ai/paper/2010.11300","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.11300"}},"official":{"repos":["TURuibo/long-term-impact-of-fairness-constraints"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/incorporating-interpretable-output","slug":"incorporating-interpretable-output","title":"Incorporating Interpretable Output Constraints in Bayesian Neural Networks","date":"2020-10-21","arxiv_id":"2010.10969","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/incorporating-interpretable-output#ran","syntology_url":"https://syntology.ai/paper/2010.10969","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.10969"}},"official":{"repos":["dtak/ocbnn-public"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/robustbench-a-standardized-adversarial","slug":"robustbench-a-standardized-adversarial","title":"RobustBench: a standardized adversarial robustness benchmark","date":"2020-10-19","arxiv_id":"2010.09670","repositories_listed":1,"syntology":null},{"url":"/paper/exchanging-lessons-between-algorithmic-1","slug":"exchanging-lessons-between-algorithmic-1","title":"Environment Inference for Invariant Learning","date":"2020-10-14","arxiv_id":"2010.07249","repositories_listed":1,"syntology":null},{"url":"/paper/fairness-in-streaming-submodular-maximization","slug":"fairness-in-streaming-submodular-maximization","title":"Fairness in Streaming Submodular Maximization: Algorithms and Hardness","date":"2020-10-14","arxiv_id":"2010.07431","repositories_listed":1,"syntology":null},{"url":"/paper/fair-n-fair-and-robust-neural-networks-for","slug":"fair-n-fair-and-robust-neural-networks-for","title":"FaiR-N: Fair and Robust Neural Networks for Structured Data","date":"2020-10-13","arxiv_id":"2010.06113","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-fairness-of-causal-algorithmic","slug":"on-the-fairness-of-causal-algorithmic","title":"On the Fairness of Causal Algorithmic Recourse","date":"2020-10-13","arxiv_id":"2010.06529","repositories_listed":1,"syntology":null},{"url":"/paper/robust-fairness-under-covariate-shift","slug":"robust-fairness-under-covariate-shift","title":"Robust Fairness under Covariate Shift","date":"2020-10-11","arxiv_id":"2010.05166","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/robust-fairness-under-covariate-shift#ran","syntology_url":"https://syntology.ai/paper/2010.05166","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.05166"}},"official":{"repos":["arezae4/fair_covariate_shift"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/streaming-submodular-maximization-with","slug":"streaming-submodular-maximization-with","title":"Fair and Representative Subset Selection from Data Streams","date":"2020-10-09","arxiv_id":"2010.04412","repositories_listed":1,"syntology":null},{"url":"/paper/learning-the-pareto-front-with-hypernetworks-1","slug":"learning-the-pareto-front-with-hypernetworks-1","title":"Learning the Pareto Front with Hypernetworks","date":"2020-10-08","arxiv_id":"2010.04104","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-the-pareto-front-with-hypernetworks-1#ran","syntology_url":"https://syntology.ai/paper/2010.04104","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.04104"}},"official":{"repos":["AvivNavon/pareto-hypernetworks"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/exact-symbolic-inference-in-probabilistic","slug":"exact-symbolic-inference-in-probabilistic","title":"SPPL: Probabilistic Programming with Fast Exact Symbolic Inference","date":"2020-10-07","arxiv_id":"2010.03485","repositories_listed":1,"syntology":{"n":30,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":20,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 20 unverified","sample_list":"/paper/exact-symbolic-inference-in-probabilistic#ran","syntology_url":"https://syntology.ai/paper/2010.03485","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.03485"}},"official":{"repos":["probcomp/sppl"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":20,"ran_from_kinds":["official"]}}},{"url":"/paper/astraea-grammar-based-fairness-testing","slug":"astraea-grammar-based-fairness-testing","title":"Astraea: Grammar-based Fairness Testing","date":"2020-10-06","arxiv_id":"2010.02542","repositories_listed":1,"syntology":null},{"url":"/paper/a-primal-dual-subgradient-approach-for-fair","slug":"a-primal-dual-subgradient-approach-for-fair","title":"A Primal-Dual Subgradient Approachfor Fair Meta Learning","date":"2020-09-26","arxiv_id":"2009.12675","repositories_listed":1,"syntology":null},{"url":"/paper/the-struggles-of-feature-based-explanations","slug":"the-struggles-of-feature-based-explanations","title":"The Struggles of Feature-Based Explanations: Shapley Values vs. Minimal Sufficient Subsets","date":"2020-09-23","arxiv_id":"2009.11023","repositories_listed":1,"syntology":null},{"url":"/paper/justicia-a-stochastic-sat-approach-to","slug":"justicia-a-stochastic-sat-approach-to","title":"Justicia: A Stochastic SAT Approach to Formally Verify Fairness","date":"2020-09-14","arxiv_id":"2009.06516","repositories_listed":1,"syntology":null},{"url":"/paper/learning-unbiased-representations-via-renyi","slug":"learning-unbiased-representations-via-renyi","title":"Learning Unbiased Representations via Rényi Minimization","date":"2020-09-07","arxiv_id":"2009.03183","repositories_listed":1,"syntology":null},{"url":"/paper/initial-classifier-weights-replay-for","slug":"initial-classifier-weights-replay-for","title":"Initial Classifier Weights Replay for Memoryless Class Incremental Learning","date":"2020-08-31","arxiv_id":"2008.13710","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-learning-for-counterfactual","slug":"adversarial-learning-for-counterfactual","title":"Adversarial Learning for Counterfactual Fairness","date":"2020-08-30","arxiv_id":"2008.13122","repositories_listed":1,"syntology":null},{"url":"/paper/collaborative-fairness-in-federated-learning","slug":"collaborative-fairness-in-federated-learning","title":"Collaborative Fairness in Federated Learning","date":"2020-08-27","arxiv_id":"2008.12161","repositories_listed":1,"syntology":null},{"url":"/paper/improving-fair-predictions-using-variational","slug":"improving-fair-predictions-using-variational","title":"Improving Fair Predictions Using Variational Inference In Causal Models","date":"2020-08-25","arxiv_id":"2008.10880","repositories_listed":1,"syntology":null},{"url":"/paper/learning-fair-policies-in-multiobjective-deep","slug":"learning-fair-policies-in-multiobjective-deep","title":"Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted Rewards","date":"2020-08-18","arxiv_id":"2008.07773","repositories_listed":1,"syntology":null},{"url":"/paper/null-sampling-for-interpretable-and-fair","slug":"null-sampling-for-interpretable-and-fair","title":"Null-sampling for Interpretable and Fair Representations","date":"2020-08-12","arxiv_id":"2008.05248","repositories_listed":1,"syntology":null},{"url":"/paper/accuracy-and-fairness-trade-offs-in-machine","slug":"accuracy-and-fairness-trade-offs-in-machine","title":"Accuracy and Fairness Trade-offs in Machine Learning: A Stochastic Multi-Objective Approach","date":"2020-08-03","arxiv_id":"2008.01132","repositories_listed":1,"syntology":null},{"url":"/paper/denoising-individual-bias-for-a-fairer-binary","slug":"denoising-individual-bias-for-a-fairer-binary","title":"Denoising individual bias for a fairer binary submatrix detection","date":"2020-07-31","arxiv_id":"2007.15816","repositories_listed":1,"syntology":null},{"url":"/paper/fairness-aware-online-personalization","slug":"fairness-aware-online-personalization","title":"Fairness-Aware Online Personalization","date":"2020-07-30","arxiv_id":"2007.15270","repositories_listed":1,"syntology":null},{"url":"/paper/murtree-optimal-classification-trees-via","slug":"murtree-optimal-classification-trees-via","title":"MurTree: Optimal Classification Trees via Dynamic Programming and Search","date":"2020-07-24","arxiv_id":"2007.12652","repositories_listed":1,"syntology":null},{"url":"/paper/socrates-towards-a-unified-platform-for","slug":"socrates-towards-a-unified-platform-for","title":"SOCRATES: Towards a Unified Platform for Neural Network Analysis","date":"2020-07-22","arxiv_id":"2007.11206","repositories_listed":1,"syntology":null},{"url":"/paper/an-empirical-characterization-of-fair-machine","slug":"an-empirical-characterization-of-fair-machine","title":"An Empirical Characterization of Fair Machine Learning For Clinical Risk Prediction","date":"2020-07-20","arxiv_id":"2007.10306","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/an-empirical-characterization-of-fair-machine#ran","syntology_url":"https://syntology.ai/paper/2007.10306","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.10306"}},"official":{"repos":["som-shahlab/fairness_benchmark"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/joint-trajectory-and-passive-beamforming","slug":"joint-trajectory-and-passive-beamforming","title":"Joint Trajectory and Passive Beamforming Design for Intelligent Reflecting Surface-Aided UAV Communications: A Deep Reinforcement Learning Approach","date":"2020-07-16","arxiv_id":"2007.08380","repositories_listed":1,"syntology":null},{"url":"/paper/towards-causal-benchmarking-of-bias-in-face","slug":"towards-causal-benchmarking-of-bias-in-face","title":"Towards causal benchmarking of bias in face analysis algorithms","date":"2020-07-13","arxiv_id":"2007.06570","repositories_listed":1,"syntology":null},{"url":"/paper/lfq-online-learning-of-per-flow-queuing","slug":"lfq-online-learning-of-per-flow-queuing","title":"LFQ: Online Learning of Per-flow Queuing Policies using Deep Reinforcement Learning","date":"2020-07-06","arxiv_id":"2007.02735","repositories_listed":1,"syntology":null},{"url":"/paper/distributional-individual-fairness-in","slug":"distributional-individual-fairness-in","title":"Distributional Individual Fairness in Clustering","date":"2020-06-22","arxiv_id":"2006.12589","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/distributional-individual-fairness-in#ran","syntology_url":"https://syntology.ai/paper/2006.12589","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.12589"}},"official":{"repos":["nihesh/distributional_individual_fairness_in_clustering"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/how-fair-can-we-go-in-machine-learning","slug":"how-fair-can-we-go-in-machine-learning","title":"How fair can we go in machine learning? Assessing the boundaries of fairness in decision trees","date":"2020-06-22","arxiv_id":"2006.12399","repositories_listed":1,"syntology":null},{"url":"/paper/improving-lime-robustness-with-smarter","slug":"improving-lime-robustness-with-smarter","title":"Improving LIME Robustness with Smarter Locality Sampling","date":"2020-06-22","arxiv_id":"2006.12302","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/improving-lime-robustness-with-smarter#ran","syntology_url":"https://syntology.ai/paper/2006.12302","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.12302"}},"official":{"repos":["seansaito/Faster-LIME"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fair-active-learning-1","slug":"fair-active-learning-1","title":"Fair Active Learning","date":"2020-06-20","arxiv_id":"2006.13025","repositories_listed":1,"syntology":null},{"url":"/paper/algorithmic-decision-making-with-conditional","slug":"algorithmic-decision-making-with-conditional","title":"Algorithmic Decision Making with Conditional Fairness","date":"2020-06-18","arxiv_id":"2006.10483","repositories_listed":1,"syntology":null},{"url":"/paper/fairness-through-robustness-investigating","slug":"fairness-through-robustness-investigating","title":"Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning","date":"2020-06-17","arxiv_id":"2006.12621","repositories_listed":1,"syntology":null},{"url":"/paper/on-adversarial-bias-and-the-robustness-of","slug":"on-adversarial-bias-and-the-robustness-of","title":"On Adversarial Bias and the Robustness of Fair Machine Learning","date":"2020-06-15","arxiv_id":"2006.08669","repositories_listed":1,"syntology":null},{"url":"/paper/xorder-a-model-agnostic-post-processing","slug":"xorder-a-model-agnostic-post-processing","title":"Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility","date":"2020-06-15","arxiv_id":"2006.08267","repositories_listed":1,"syntology":null},{"url":"/paper/quota-based-debiasing-can-decrease","slug":"quota-based-debiasing-can-decrease","title":"Quota-based debiasing can decrease representation of already underrepresented groups","date":"2020-06-13","arxiv_id":"2006.07647","repositories_listed":1,"syntology":null},{"url":"/paper/algorithms-and-learning-for-fair-portfolio","slug":"algorithms-and-learning-for-fair-portfolio","title":"Algorithms and Learning for Fair Portfolio Design","date":"2020-06-12","arxiv_id":"2006.07281","repositories_listed":1,"syntology":null},{"url":"/paper/a-multi-objective-based-approach-for-fair","slug":"a-multi-objective-based-approach-for-fair","title":"Analysis of Trade-offs in Fair Principal Component Analysis Based on Multi-objective Optimization","date":"2020-06-11","arxiv_id":"2006.06137","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-sampling-to-reduce-disparate","slug":"adaptive-sampling-to-reduce-disparate","title":"Active Sampling for Min-Max Fairness","date":"2020-06-11","arxiv_id":"2006.06879","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adaptive-sampling-to-reduce-disparate#ran","syntology_url":"https://syntology.ai/paper/2006.06879","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.06879"}},"official":{"repos":["amazon-research/active-sampling-for-minmax-fairness"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fair-clustering-for-diverse-and-experienced","slug":"fair-clustering-for-diverse-and-experienced","title":"Hypergraph Clustering for Finding Diverse and Experienced Groups","date":"2020-06-10","arxiv_id":"2006.05645","repositories_listed":1,"syntology":null},{"url":"/paper/achieving-equalized-odds-by-resampling","slug":"achieving-equalized-odds-by-resampling","title":"Achieving Equalized Odds by Resampling Sensitive Attributes","date":"2020-06-08","arxiv_id":"2006.04292","repositories_listed":1,"syntology":{"n":15,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/achieving-equalized-odds-by-resampling#ran","syntology_url":"https://syntology.ai/paper/2006.04292","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.04292"}},"official":{"repos":["yromano/fair_dummies"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/classification-under-misspecification","slug":"classification-under-misspecification","title":"Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Connections to Evolvability","date":"2020-06-08","arxiv_id":"2006.04787","repositories_listed":1,"syntology":null},{"url":"/paper/fair-classification-with-noisy-protected","slug":"fair-classification-with-noisy-protected","title":"Fair Classification with Noisy Protected Attributes: A Framework with Provable Guarantees","date":"2020-06-08","arxiv_id":"2006.04778","repositories_listed":1,"syntology":null},{"url":"/paper/a-machine-learning-system-for-retaining","slug":"a-machine-learning-system-for-retaining","title":"A Machine Learning System for Retaining Patients in HIV Care","date":"2020-06-01","arxiv_id":"2006.04944","repositories_listed":1,"syntology":null},{"url":"/paper/deep-fair-clustering-for-visual-learning","slug":"deep-fair-clustering-for-visual-learning","title":"Deep Fair Clustering for Visual Learning","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/controlling-fairness-and-bias-in-dynamic","slug":"controlling-fairness-and-bias-in-dynamic","title":"Controlling Fairness and Bias in Dynamic Learning-to-Rank","date":"2020-05-29","arxiv_id":"2005.14713","repositories_listed":1,"syntology":null},{"url":"/paper/ethical-adversaries-towards-mitigating","slug":"ethical-adversaries-towards-mitigating","title":"Ethical Adversaries: Towards Mitigating Unfairness with Adversarial Machine Learning","date":"2020-05-14","arxiv_id":"2005.06852","repositories_listed":1,"syntology":null},{"url":"/paper/statistical-equity-a-fairness-classification","slug":"statistical-equity-a-fairness-classification","title":"Statistical Equity: A Fairness Classification Objective","date":"2020-05-14","arxiv_id":"2005.07293","repositories_listed":1,"syntology":null},{"url":"/paper/why-fairness-cannot-be-automated-bridging-the","slug":"why-fairness-cannot-be-automated-bridging-the","title":"Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AI","date":"2020-05-12","arxiv_id":"2005.05906","repositories_listed":1,"syntology":null},{"url":"/paper/cyberbullying-detection-with-fairness","slug":"cyberbullying-detection-with-fairness","title":"Cyberbullying Detection with Fairness Constraints","date":"2020-05-09","arxiv_id":"2005.06625","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-graph-embeddings-for-fair","slug":"adversarial-graph-embeddings-for-fair","title":"Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks","date":"2020-05-08","arxiv_id":"2005.04074","repositories_listed":1,"syntology":null},{"url":"/paper/in-pursuit-of-interpretable-fair-and-accurate","slug":"in-pursuit-of-interpretable-fair-and-accurate","title":"In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction","date":"2020-05-08","arxiv_id":"2005.04176","repositories_listed":1,"syntology":null},{"url":"/paper/comparing-snns-and-rnns-on-neuromorphic","slug":"comparing-snns-and-rnns-on-neuromorphic","title":"Comparing SNNs and RNNs on Neuromorphic Vision Datasets: Similarities and Differences","date":"2020-05-02","arxiv_id":"2005.02183","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"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) · 0 unverified","sample_list":"/paper/comparing-snns-and-rnns-on-neuromorphic#ran","syntology_url":"https://syntology.ai/paper/2005.02183","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.02183"}},"official":null}},{"url":"/paper/curiosity-driven-energy-efficient-worker","slug":"curiosity-driven-energy-efficient-worker","title":"Curiosity-Driven Energy-Efficient Worker Scheduling in Vehicular Crowdsourcing: A Deep Reinforcement Learning Approach","date":"2020-04-24","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/fairness-in-bio-inspired-optimization","slug":"fairness-in-bio-inspired-optimization","title":"A Prescription of Methodological Guidelines for Comparing Bio-inspired Optimization Algorithms","date":"2020-04-19","arxiv_id":"2004.09969","repositories_listed":1,"syntology":null},{"url":"/paper/poisoning-attacks-on-algorithmic-fairness","slug":"poisoning-attacks-on-algorithmic-fairness","title":"Poisoning Attacks on Algorithmic Fairness","date":"2020-04-15","arxiv_id":"2004.07401","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/poisoning-attacks-on-algorithmic-fairness#ran","syntology_url":"https://syntology.ai/paper/2004.07401","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.07401"}},"official":{"repos":["dsolanno/Poisoning-Attacks-on-Algorithmic-Fairness"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/entity-switched-datasets-an-approach-to","slug":"entity-switched-datasets-an-approach-to","title":"Entity-Switched Datasets: An Approach to Auditing the In-Domain Robustness of Named Entity Recognition Models","date":"2020-04-08","arxiv_id":"2004.04123","repositories_listed":1,"syntology":null},{"url":"/paper/model-agnostic-characterization-of-fairness","slug":"model-agnostic-characterization-of-fairness","title":"FACT: A Diagnostic for Group Fairness Trade-offs","date":"2020-04-07","arxiv_id":"2004.03424","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":0,"n_instrument":6,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"6 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; 6 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/model-agnostic-characterization-of-fairness#ran","syntology_url":"https://syntology.ai/paper/2004.03424","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.03424"}},"official":{"repos":["wnstlr/FACT"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/fairnn-conjoint-learning-of-fair","slug":"fairnn-conjoint-learning-of-fair","title":"FairNN- Conjoint Learning of Fair Representations for Fair Decisions","date":"2020-04-05","arxiv_id":"2004.02173","repositories_listed":1,"syntology":null},{"url":"/paper/fairalm-augmented-lagrangian-method-for","slug":"fairalm-augmented-lagrangian-method-for","title":"FairALM: Augmented Lagrangian Method for Training Fair Models with Little Regret","date":"2020-04-03","arxiv_id":"2004.01355","repositories_listed":1,"syntology":null},{"url":"/paper/face-quality-estimation-and-its-correlation","slug":"face-quality-estimation-and-its-correlation","title":"Face Quality Estimation and Its Correlation to Demographic and Non-Demographic Bias in Face Recognition","date":"2020-04-02","arxiv_id":"2004.01019","repositories_listed":1,"syntology":null},{"url":"/paper/balancing-competing-objectives-with-noisy","slug":"balancing-competing-objectives-with-noisy","title":"Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning","date":"2020-03-15","arxiv_id":"2003.06740","repositories_listed":1,"syntology":null},{"url":"/paper/learning-unbiased-representations-via-mutual","slug":"learning-unbiased-representations-via-mutual","title":"Learning Unbiased Representations via Mutual Information Backpropagation","date":"2020-03-13","arxiv_id":"2003.06430","repositories_listed":1,"syntology":null},{"url":"/paper/fairness-by-learning-orthogonal-disentangled","slug":"fairness-by-learning-orthogonal-disentangled","title":"Fairness by Learning Orthogonal Disentangled Representations","date":"2020-03-12","arxiv_id":"2003.05707","repositories_listed":1,"syntology":null},{"url":"/paper/hurtful-words-quantifying-biases-in-clinical","slug":"hurtful-words-quantifying-biases-in-clinical","title":"Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings","date":"2020-03-11","arxiv_id":"2003.11515","repositories_listed":1,"syntology":null},{"url":"/paper/addressing-multiple-metrics-of-group-fairness","slug":"addressing-multiple-metrics-of-group-fairness","title":"Addressing multiple metrics of group fairness in data-driven decision making","date":"2020-03-10","arxiv_id":"2003.04794","repositories_listed":1,"syntology":null},{"url":"/paper/exactly-computing-the-local-lipschitz","slug":"exactly-computing-the-local-lipschitz","title":"Exactly Computing the Local Lipschitz Constant of ReLU Networks","date":"2020-03-02","arxiv_id":"2003.01219","repositories_listed":1,"syntology":null},{"url":"/paper/debayes-a-bayesian-method-for-debiasing","slug":"debayes-a-bayesian-method-for-debiasing","title":"DeBayes: a Bayesian Method for Debiasing Network Embeddings","date":"2020-02-26","arxiv_id":"2002.11442","repositories_listed":1,"syntology":null},{"url":"/paper/fr-train-a-mutual-information-based-approach","slug":"fr-train-a-mutual-information-based-approach","title":"FR-Train: A Mutual Information-Based Approach to Fair and Robust Training","date":"2020-02-24","arxiv_id":"2002.10234","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"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) · 0 unverified","sample_list":"/paper/fr-train-a-mutual-information-based-approach#ran","syntology_url":"https://syntology.ai/paper/2002.10234","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.10234"}},"official":{"repos":["yuji-roh/fr-train"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-certified-individually-fair","slug":"learning-certified-individually-fair","title":"Learning Certified Individually Fair Representations","date":"2020-02-24","arxiv_id":"2002.10312","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/learning-certified-individually-fair#ran","syntology_url":"https://syntology.ai/paper/2002.10312","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.10312"}},"official":{"repos":["eth-sri/lcifr"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/robust-optimization-for-fairness-with-noisy","slug":"robust-optimization-for-fairness-with-noisy","title":"Robust Optimization for Fairness with Noisy Protected Groups","date":"2020-02-21","arxiv_id":"2002.09343","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"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) · 2 unverified","sample_list":"/paper/robust-optimization-for-fairness-with-noisy#ran","syntology_url":"https://syntology.ai/paper/2002.09343","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.09343"}},"official":{"repos":["wenshuoguo/robust-fairness-code"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/data-and-model-dependencies-of-membership","slug":"data-and-model-dependencies-of-membership","title":"Data and Model Dependencies of Membership Inference Attack","date":"2020-02-17","arxiv_id":"2002.06856","repositories_listed":1,"syntology":null},{"url":"/paper/individual-fairness-for-k-clustering","slug":"individual-fairness-for-k-clustering","title":"Individual Fairness for $k$-Clustering","date":"2020-02-17","arxiv_id":"2002.06742","repositories_listed":1,"syntology":null},{"url":"/paper/chexclusion-fairness-gaps-in-deep-chest-x-ray","slug":"chexclusion-fairness-gaps-in-deep-chest-x-ray","title":"CheXclusion: Fairness gaps in deep chest X-ray classifiers","date":"2020-02-14","arxiv_id":"2003.00827","repositories_listed":1,"syntology":null},{"url":"/paper/fast-fair-regression-via-efficient","slug":"fast-fair-regression-via-efficient","title":"Fast Fair Regression via Efficient Approximations of Mutual Information","date":"2020-02-14","arxiv_id":"2002.06200","repositories_listed":1,"syntology":null},{"url":"/paper/post-comparison-mitigation-of-demographic","slug":"post-comparison-mitigation-of-demographic","title":"Post-Comparison Mitigation of Demographic Bias in Face Recognition Using Fair Score Normalization","date":"2020-02-10","arxiv_id":"2002.03592","repositories_listed":1,"syntology":null},{"url":"/paper/oblivious-data-for-fairness-with-kernels","slug":"oblivious-data-for-fairness-with-kernels","title":"Oblivious Data for Fairness with Kernels","date":"2020-02-07","arxiv_id":"2002.02901","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/oblivious-data-for-fairness-with-kernels#ran","syntology_url":"https://syntology.ai/paper/2002.02901","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.02901"}},"official":{"repos":["azalk/Oblivious"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fair-correlation-clustering","slug":"fair-correlation-clustering","title":"Fair Correlation Clustering","date":"2020-02-06","arxiv_id":"2002.02274","repositories_listed":1,"syntology":null},{"url":"/paper/additive-tree-ensembles-reasoning-about","slug":"additive-tree-ensembles-reasoning-about","title":"Verifying Tree Ensembles by Reasoning about Potential Instances","date":"2020-01-31","arxiv_id":"2001.11905","repositories_listed":1,"syntology":null},{"url":"/paper/deontological-ethics-by-monotonicity-shape","slug":"deontological-ethics-by-monotonicity-shape","title":"Deontological Ethics By Monotonicity Shape Constraints","date":"2020-01-31","arxiv_id":"2001.11990","repositories_listed":1,"syntology":null},{"url":"/paper/case-study-predictive-fairness-to-reduce","slug":"case-study-predictive-fairness-to-reduce","title":"Case Study: Predictive Fairness to Reduce Misdemeanor Recidivism Through Social Service Interventions","date":"2020-01-24","arxiv_id":"2001.09233","repositories_listed":1,"syntology":null},{"url":"/paper/teaching-software-engineering-for-ai-enabled","slug":"teaching-software-engineering-for-ai-enabled","title":"Teaching Software Engineering for AI-Enabled Systems","date":"2020-01-18","arxiv_id":"2001.06691","repositories_listed":1,"syntology":null},{"url":"/paper/robbert-a-dutch-roberta-based-language-model","slug":"robbert-a-dutch-roberta-based-language-model","title":"RobBERT: a Dutch RoBERTa-based Language Model","date":"2020-01-17","arxiv_id":"2001.06286","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/robbert-a-dutch-roberta-based-language-model#ran","syntology_url":"https://syntology.ai/paper/2001.06286","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2001.06286"}},"official":{"repos":["iPieter/RobBERT"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fair-active-learning","slug":"fair-active-learning","title":"Fair Active Learning","date":"2020-01-06","arxiv_id":"2001.01796","repositories_listed":1,"syntology":null},{"url":"/paper/a-framework-for-democratizing-ai","slug":"a-framework-for-democratizing-ai","title":"A Framework for Democratizing AI","date":"2020-01-01","arxiv_id":"2001.00818","repositories_listed":1,"syntology":null},{"url":"/paper/bounding-the-fairness-and-accuracy-of","slug":"bounding-the-fairness-and-accuracy-of","title":"Bounding the fairness and accuracy of classifiers from population statistics","date":"2020-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/too-relaxed-to-be-fair","slug":"too-relaxed-to-be-fair","title":"Too Relaxed to Be Fair","date":"2020-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/search-based-repair-of-deep-neural-networks","slug":"search-based-repair-of-deep-neural-networks","title":"Arachne: Search Based Repair of Deep Neural Networks","date":"2019-12-28","arxiv_id":"1912.12463","repositories_listed":1,"syntology":null},{"url":"/paper/balancing-the-tradeoff-between-profit-and","slug":"balancing-the-tradeoff-between-profit-and","title":"Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms During High-Demand Hours","date":"2019-12-18","arxiv_id":"1912.08388","repositories_listed":1,"syntology":null},{"url":"/paper/group-fairness-in-bandit-arm-selection","slug":"group-fairness-in-bandit-arm-selection","title":"Group Fairness in Bandit Arm Selection","date":"2019-12-09","arxiv_id":"1912.03802","repositories_listed":1,"syntology":null},{"url":"/paper/perfectly-parallel-fairness-certification-of","slug":"perfectly-parallel-fairness-certification-of","title":"Perfectly Parallel Fairness Certification of Neural Networks","date":"2019-12-05","arxiv_id":"1912.02499","repositories_listed":1,"syntology":null},{"url":"/paper/addressing-marketing-bias-in-product","slug":"addressing-marketing-bias-in-product","title":"Addressing Marketing Bias in Product Recommendations","date":"2019-12-04","arxiv_id":"1912.01799","repositories_listed":1,"syntology":null},{"url":"/paper/assessing-disparate-impact-of-personalized","slug":"assessing-disparate-impact-of-personalized","title":"Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds","date":"2019-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null}],"record_sha256":"073e9135e09ee9a669b4c5cd6bbe64dd3690a8af11a658ae0ebfe196dafe3705","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}