{"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/15","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":15,"pages_in_order":57,"rows_per_page":100,"rows":[1401,1500],"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/14","next":"/task/fairness/papers/16","papers":[{"url":"/paper/preferencenet-encoding-human-preferences-in","slug":"preferencenet-encoding-human-preferences-in","title":"PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning","date":"2021-06-06","arxiv_id":"2106.03215","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/preferencenet-encoding-human-preferences-in#ran","syntology_url":"https://syntology.ai/paper/2106.03215","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.03215"}},"official":{"repos":["neeharperi/PreferenceNet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/strategyproof-learning-building-trustworthy","slug":"strategyproof-learning-building-trustworthy","title":"Strategyproof Learning: Building Trustworthy User-Generated Datasets","date":"2021-06-04","arxiv_id":"2106.02398","repositories_listed":1,"syntology":null},{"url":"/paper/subgroup-fairness-in-two-sided-markets","slug":"subgroup-fairness-in-two-sided-markets","title":"Subgroup Fairness in Two-Sided Markets","date":"2021-06-04","arxiv_id":"2106.02702","repositories_listed":1,"syntology":null},{"url":"/paper/bifair-training-fair-models-with-bilevel","slug":"bifair-training-fair-models-with-bilevel","title":"Fair Machine Learning under Limited Demographically Labeled Data","date":"2021-06-03","arxiv_id":"2106.04757","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/bifair-training-fair-models-with-bilevel#ran","syntology_url":"https://syntology.ai/paper/2106.04757","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.04757"}},"official":{"repos":["TinfoilHat0/BiFair"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/fair-preprocessing-towards-understanding","slug":"fair-preprocessing-towards-understanding","title":"Fair Preprocessing: Towards Understanding Compositional Fairness of Data Transformers in Machine Learning Pipeline","date":"2021-06-02","arxiv_id":"2106.06054","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/fair-preprocessing-towards-understanding#ran","syntology_url":"https://syntology.ai/paper/2106.06054","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.06054"}},"official":{"repos":["sumonbis/FairPreprocessing"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/uncertainty-quantification-360-a-holistic","slug":"uncertainty-quantification-360-a-holistic","title":"Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI","date":"2021-06-02","arxiv_id":"2106.01410","repositories_listed":1,"syntology":null},{"url":"/paper/the-zoo-of-fairness-metrics-in-machine","slug":"the-zoo-of-fairness-metrics-in-machine","title":"A Clarification of the Nuances in the Fairness Metrics Landscape","date":"2021-06-01","arxiv_id":"2106.00467","repositories_listed":1,"syntology":null},{"url":"/paper/zipf-matrix-factorization-matrix","slug":"zipf-matrix-factorization-matrix","title":"Zipf Matrix Factorization : Matrix Factorization with Matthew Effect Reduction","date":"2021-06-01","arxiv_id":"2106.07347","repositories_listed":1,"syntology":null},{"url":"/paper/dissect-disentangled-simultaneous","slug":"dissect-disentangled-simultaneous","title":"DISSECT: Disentangled Simultaneous Explanations via Concept Traversals","date":"2021-05-31","arxiv_id":"2105.15164","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":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/dissect-disentangled-simultaneous#ran","syntology_url":"https://syntology.ai/paper/2105.15164","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.15164"}},"official":{"repos":["asmadotgh/dissect"],"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/shaq-incorporating-shapley-value-theory-into","slug":"shaq-incorporating-shapley-value-theory-into","title":"SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning","date":"2021-05-31","arxiv_id":"2105.15013","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":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/shaq-incorporating-shapley-value-theory-into#ran","syntology_url":"https://syntology.ai/paper/2105.15013","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.15013"}},"official":{"repos":["hsvgbkhgbv/shapley-q-learning"],"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/enumerating-fair-packages-for-group","slug":"enumerating-fair-packages-for-group","title":"Enumerating Fair Packages for Group Recommendations","date":"2021-05-30","arxiv_id":"2105.14423","repositories_listed":1,"syntology":null},{"url":"/paper/deep-fair-discriminative-clustering","slug":"deep-fair-discriminative-clustering","title":"Deep Fair Discriminative Clustering","date":"2021-05-28","arxiv_id":"2105.14146","repositories_listed":1,"syntology":null},{"url":"/paper/fawos-fairness-aware-oversampling-algorithm","slug":"fawos-fairness-aware-oversampling-algorithm","title":"FAWOS: Fairness-Aware Oversampling Algorithm Based on Distributions of Sensitive Attributes","date":"2021-05-27","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/private-recommender-systems-how-can-users","slug":"private-recommender-systems-how-can-users","title":"Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data?","date":"2021-05-26","arxiv_id":"2105.12353","repositories_listed":1,"syntology":null},{"url":"/paper/deconfounded-recommendation-for-alleviating","slug":"deconfounded-recommendation-for-alleviating","title":"Deconfounded Recommendation for Alleviating Bias Amplification","date":"2021-05-22","arxiv_id":"2105.10648","repositories_listed":1,"syntology":null},{"url":"/paper/sampling-with-trusthworthy-constraints-a-1","slug":"sampling-with-trusthworthy-constraints-a-1","title":"Sampling with Trusthworthy Constraints: A Variational Gradient Framework","date":"2021-05-21","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/towards-personalized-fairness-based-on-causal","slug":"towards-personalized-fairness-based-on-causal","title":"Personalized Counterfactual Fairness in Recommendation","date":"2021-05-20","arxiv_id":"2105.09829","repositories_listed":1,"syntology":null},{"url":"/paper/cohort-shapley-value-for-algorithmic-fairness","slug":"cohort-shapley-value-for-algorithmic-fairness","title":"Cohort Shapley value for algorithmic fairness","date":"2021-05-15","arxiv_id":"2105.07168","repositories_listed":1,"syntology":null},{"url":"/paper/addressing-fairness-bias-and-class-imbalance","slug":"addressing-fairness-bias-and-class-imbalance","title":"Addressing Fairness, Bias and Class Imbalance in Machine Learning: the FBI-loss","date":"2021-05-13","arxiv_id":"2105.06345","repositories_listed":1,"syntology":null},{"url":"/paper/an-empirical-comparison-of-bias-reduction","slug":"an-empirical-comparison-of-bias-reduction","title":"An Empirical Comparison of Bias Reduction Methods on Real-World Problems in High-Stakes Policy Settings","date":"2021-05-13","arxiv_id":"2105.06442","repositories_listed":1,"syntology":null},{"url":"/paper/causally-motivated-shortcut-removal-using","slug":"causally-motivated-shortcut-removal-using","title":"Causally motivated Shortcut Removal Using Auxiliary Labels","date":"2021-05-13","arxiv_id":"2105.06422","repositories_listed":1,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/causally-motivated-shortcut-removal-using#ran","syntology_url":"https://syntology.ai/paper/2105.06422","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.06422"}},"official":{"repos":["mymakar/causally_motivated_shortcut_removal"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/evolutionary-in-stability-of-selfish-learning","slug":"evolutionary-in-stability-of-selfish-learning","title":"Evolutionary instability of selfish learning in repeated games","date":"2021-05-13","arxiv_id":"2105.06199","repositories_listed":1,"syntology":null},{"url":"/paper/loss-aversively-fair-classification","slug":"loss-aversively-fair-classification","title":"Loss-Aversively Fair Classification","date":"2021-05-10","arxiv_id":"2105.04273","repositories_listed":1,"syntology":null},{"url":"/paper/societal-biases-in-language-generation","slug":"societal-biases-in-language-generation","title":"Societal Biases in Language Generation: Progress and Challenges","date":"2021-05-10","arxiv_id":"2105.04054","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":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) · 0 unverified","sample_list":"/paper/societal-biases-in-language-generation#ran","syntology_url":"https://syntology.ai/paper/2105.04054","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.04054"}},"official":{"repos":["ewsheng/decoding-biases"],"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/loss-tolerant-federated-learning","slug":"loss-tolerant-federated-learning","title":"Loss Tolerant Federated Learning","date":"2021-05-08","arxiv_id":"2105.03591","repositories_listed":1,"syntology":null},{"url":"/paper/pairwise-fairness-for-ordinal-regression","slug":"pairwise-fairness-for-ordinal-regression","title":"Pairwise Fairness for Ordinal Regression","date":"2021-05-07","arxiv_id":"2105.03153","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pairwise-fairness-for-ordinal-regression#ran","syntology_url":"https://syntology.ai/paper/2105.03153","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.03153"}},"official":{"repos":["amazon-research/fair-ordinal-regression"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/crosswalk-fairness-enhanced-node","slug":"crosswalk-fairness-enhanced-node","title":"CrossWalk: Fairness-enhanced Node Representation Learning","date":"2021-05-06","arxiv_id":"2105.02725","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/crosswalk-fairness-enhanced-node#ran","syntology_url":"https://syntology.ai/paper/2105.02725","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.02725"}},"official":{"repos":["ahmadkhajehnejad/CrossWalk"],"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/when-fair-ranking-meets-uncertain-inference","slug":"when-fair-ranking-meets-uncertain-inference","title":"When Fair Ranking Meets Uncertain Inference","date":"2021-05-05","arxiv_id":"2105.02091","repositories_listed":1,"syntology":null},{"url":"/paper/computationally-efficient-optimization-of-1","slug":"computationally-efficient-optimization-of-1","title":"Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness","date":"2021-05-03","arxiv_id":"2105.00855","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/computationally-efficient-optimization-of-1#ran","syntology_url":"https://syntology.ai/paper/2105.00855","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.00855"}},"official":{"repos":["HarrieO/2021-SIGIR-plackett-luce"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-to-continuously-optimize-wireless-1","slug":"learning-to-continuously-optimize-wireless-1","title":"Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective","date":"2021-05-03","arxiv_id":"2105.01696","repositories_listed":1,"syntology":null},{"url":"/paper/federated-learning-with-fair-averaging","slug":"federated-learning-with-fair-averaging","title":"Federated Learning with Fair Averaging","date":"2021-04-30","arxiv_id":"2104.14937","repositories_listed":1,"syntology":null},{"url":"/paper/biased-edge-dropout-for-enhancing-fairness-in","slug":"biased-edge-dropout-for-enhancing-fairness-in","title":"FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning","date":"2021-04-29","arxiv_id":"2104.14210","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/biased-edge-dropout-for-enhancing-fairness-in#ran","syntology_url":"https://syntology.ai/paper/2104.14210","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.14210"}},"official":null}},{"url":"/paper/you-can-still-achieve-fairness-without","slug":"you-can-still-achieve-fairness-without","title":"Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Features","date":"2021-04-29","arxiv_id":"2104.14537","repositories_listed":1,"syntology":null},{"url":"/paper/societal-biases-in-retrieved-contents","slug":"societal-biases-in-retrieved-contents","title":"Societal Biases in Retrieved Contents: Measurement Framework and Adversarial Mitigation for BERT Rankers","date":"2021-04-28","arxiv_id":"2104.13640","repositories_listed":1,"syntology":null},{"url":"/paper/precarity-modeling-the-long-term-effects-of","slug":"precarity-modeling-the-long-term-effects-of","title":"Precarity: Modeling the Long Term Effects of Compounded Decisions on Individual Instability","date":"2021-04-24","arxiv_id":"2104.12037","repositories_listed":1,"syntology":null},{"url":"/paper/discovering-classification-rules-for","slug":"discovering-classification-rules-for","title":"Rule Generation for Classification: Scalability, Interpretability, and Fairness","date":"2021-04-21","arxiv_id":"2104.10751","repositories_listed":1,"syntology":null},{"url":"/paper/user-oriented-fairness-in-recommendation","slug":"user-oriented-fairness-in-recommendation","title":"User-oriented Fairness in Recommendation","date":"2021-04-21","arxiv_id":"2104.10671","repositories_listed":1,"syntology":null},{"url":"/paper/fair-representation-learning-for","slug":"fair-representation-learning-for","title":"Fair Representation Learning for Heterogeneous Information Networks","date":"2021-04-18","arxiv_id":"2104.08769","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/fair-representation-learning-for#ran","syntology_url":"https://syntology.ai/paper/2104.08769","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.08769"}},"official":{"repos":["HKUST-KnowComp/Fair_HIN"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/fcpso-em-leveraging-momentum-constriction","slug":"fcpso-em-leveraging-momentum-constriction","title":"Fairly Constricted Multi-Objective Particle Swarm Optimization","date":"2021-04-10","arxiv_id":"2104.10040","repositories_listed":1,"syntology":null},{"url":"/paper/verb-visualizing-and-interpreting-bias","slug":"verb-visualizing-and-interpreting-bias","title":"VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representations","date":"2021-04-06","arxiv_id":"2104.02797","repositories_listed":1,"syntology":null},{"url":"/paper/alue-arabic-language-understanding-evaluation","slug":"alue-arabic-language-understanding-evaluation","title":"ALUE: Arabic Language Understanding Evaluation","date":"2021-04-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/fairmodels-a-flexible-tool-for-bias-detection","slug":"fairmodels-a-flexible-tool-for-bias-detection","title":"fairmodels: A Flexible Tool For Bias Detection, Visualization, And Mitigation","date":"2021-04-01","arxiv_id":"2104.00507","repositories_listed":1,"syntology":null},{"url":"/paper/statistical-inference-for-individual-fairness-1","slug":"statistical-inference-for-individual-fairness-1","title":"Statistical inference for individual fairness","date":"2021-03-30","arxiv_id":"2103.16714","repositories_listed":1,"syntology":null},{"url":"/paper/federated-learning-with-taskonomy-for-non-iid","slug":"federated-learning-with-taskonomy-for-non-iid","title":"Federated Learning with Taskonomy for Non-IID Data","date":"2021-03-29","arxiv_id":"2103.15947","repositories_listed":1,"syntology":null},{"url":"/paper/can-vision-transformers-learn-without-natural","slug":"can-vision-transformers-learn-without-natural","title":"Can Vision Transformers Learn without Natural Images?","date":"2021-03-24","arxiv_id":"2103.13023","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":6,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/can-vision-transformers-learn-without-natural#ran","syntology_url":"https://syntology.ai/paper/2103.13023","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.13023"}},"official":null}},{"url":"/paper/dynamic-slimmable-network","slug":"dynamic-slimmable-network","title":"Dynamic Slimmable Network","date":"2021-03-24","arxiv_id":"2103.13258","repositories_listed":1,"syntology":{"n":17,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":10,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":17,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 10 unverified","sample_list":"/paper/dynamic-slimmable-network#ran","syntology_url":"https://syntology.ai/paper/2103.13258","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.13258"}},"official":{"repos":["changlin31/DS-Net"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":10,"ran_from_kinds":["official"]}}},{"url":"/paper/refining-neural-networks-with-compositional","slug":"refining-neural-networks-with-compositional","title":"Refining Language Models with Compositional Explanations","date":"2021-03-18","arxiv_id":"2103.10415","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 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; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/refining-neural-networks-with-compositional#ran","syntology_url":"https://syntology.ai/paper/2103.10415","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.10415"}},"official":{"repos":["INK-USC/expl-refinement"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/pros-and-cons-of-gan-evaluation-measures-new","slug":"pros-and-cons-of-gan-evaluation-measures-new","title":"Pros and Cons of GAN Evaluation Measures: New Developments","date":"2021-03-17","arxiv_id":"2103.09396","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"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) · 1 unverified","sample_list":"/paper/pros-and-cons-of-gan-evaluation-measures-new#ran","syntology_url":"https://syntology.ai/paper/2103.09396","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.09396"}},"official":{"repos":["clovaai/generative-evaluation-prdc"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/flexible-fond-planning-with-explicit-fairness","slug":"flexible-fond-planning-with-explicit-fairness","title":"Flexible FOND Planning with Explicit Fairness Assumptions","date":"2021-03-15","arxiv_id":"2103.08391","repositories_listed":1,"syntology":null},{"url":"/paper/fairness-aware-personalized-ranking","slug":"fairness-aware-personalized-ranking","title":"Fairness-aware Personalized Ranking Recommendation via Adversarial Learning","date":"2021-03-14","arxiv_id":"2103.07849","repositories_listed":1,"syntology":null},{"url":"/paper/fair-mixup-fairness-via-interpolation-1","slug":"fair-mixup-fairness-via-interpolation-1","title":"Fair Mixup: Fairness via Interpolation","date":"2021-03-11","arxiv_id":"2103.06503","repositories_listed":1,"syntology":null},{"url":"/paper/estimating-and-improving-fairness-with","slug":"estimating-and-improving-fairness-with","title":"Estimating and Improving Fairness with Adversarial Learning","date":"2021-03-07","arxiv_id":"2103.04243","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":3,"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/estimating-and-improving-fairness-with#ran","syntology_url":"https://syntology.ai/paper/2103.04243","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.04243"}},"official":null}},{"url":"/paper/elliot-a-comprehensive-and-rigorous-framework","slug":"elliot-a-comprehensive-and-rigorous-framework","title":"Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation","date":"2021-03-03","arxiv_id":"2103.02590","repositories_listed":1,"syntology":null},{"url":"/paper/fairness-and-robustness-of-contrasting","slug":"fairness-and-robustness-of-contrasting","title":"Evaluating Robustness of Counterfactual Explanations","date":"2021-03-03","arxiv_id":"2103.02354","repositories_listed":1,"syntology":null},{"url":"/paper/fairness-in-credit-scoring-assessment","slug":"fairness-in-credit-scoring-assessment","title":"Fairness in Credit Scoring: Assessment, Implementation and Profit Implications","date":"2021-03-02","arxiv_id":"2103.01907","repositories_listed":1,"syntology":null},{"url":"/paper/fairness-semi-supervised-learning-and-more-a","slug":"fairness-semi-supervised-learning-and-more-a","title":"Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints","date":"2021-03-02","arxiv_id":"2103.02013","repositories_listed":1,"syntology":null},{"url":"/paper/the-kl-divergence-between-a-graph-model-and","slug":"the-kl-divergence-between-a-graph-model-and","title":"The KL-Divergence between a Graph Model and its Fair I-Projection as a Fairness Regularizer","date":"2021-03-02","arxiv_id":"2103.01846","repositories_listed":1,"syntology":null},{"url":"/paper/towards-unbiased-and-accurate-deferral-to","slug":"towards-unbiased-and-accurate-deferral-to","title":"Towards Unbiased and Accurate Deferral to Multiple Experts","date":"2021-02-25","arxiv_id":"2102.13004","repositories_listed":1,"syntology":null},{"url":"/paper/classification-with-abstention-but-without","slug":"classification-with-abstention-but-without","title":"Classification with abstention but without disparities","date":"2021-02-24","arxiv_id":"2102.12258","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/classification-with-abstention-but-without#ran","syntology_url":"https://syntology.ai/paper/2102.12258","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.12258"}},"official":{"repos":["evgchz/dpabst"],"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/directional-bias-amplification","slug":"directional-bias-amplification","title":"Directional Bias Amplification","date":"2021-02-24","arxiv_id":"2102.12594","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/directional-bias-amplification#ran","syntology_url":"https://syntology.ai/paper/2102.12594","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.12594"}},"official":{"repos":["princetonvisualai/directional-bias-amp"],"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/fermi-fair-empirical-risk-minimization-via","slug":"fermi-fair-empirical-risk-minimization-via","title":"A Stochastic Optimization Framework for Fair Risk Minimization","date":"2021-02-24","arxiv_id":"2102.12586","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/fermi-fair-empirical-risk-minimization-via#ran","syntology_url":"https://syntology.ai/paper/2102.12586","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.12586"}},"official":{"repos":["optimization-for-data-driven-science/FERMI"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-fair-representations-for-bipartite","slug":"learning-fair-representations-for-bipartite","title":"Learning Fair Representations for Recommendation: A Graph-based Perspective","date":"2021-02-18","arxiv_id":"2102.09140","repositories_listed":1,"syntology":null},{"url":"/paper/maximizing-marginal-fairness-for-dynamic","slug":"maximizing-marginal-fairness-for-dynamic","title":"Maximizing Marginal Fairness for Dynamic Learning to Rank","date":"2021-02-18","arxiv_id":"2102.09670","repositories_listed":1,"syntology":null},{"url":"/paper/capc-learning-confidential-and-private-1","slug":"capc-learning-confidential-and-private-1","title":"CaPC Learning: Confidential and Private Collaborative Learning","date":"2021-02-09","arxiv_id":"2102.05188","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":1,"n_instrument":5,"n_unverified":3,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 5 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/capc-learning-confidential-and-private-1#ran","syntology_url":"https://syntology.ai/paper/2102.05188","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.05188"}},"official":{"repos":["cleverhans-lab/capc-iclr"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/the-fairceptron-a-framework-for-measuring","slug":"the-fairceptron-a-framework-for-measuring","title":"The FairCeptron: A Framework for Measuring Human Perceptions of Algorithmic Fairness","date":"2021-02-08","arxiv_id":"2102.04119","repositories_listed":1,"syntology":null},{"url":"/paper/removing-biased-data-to-improve-fairness-and","slug":"removing-biased-data-to-improve-fairness-and","title":"Removing biased data to improve fairness and accuracy","date":"2021-02-05","arxiv_id":"2102.03054","repositories_listed":1,"syntology":null},{"url":"/paper/re-replication-study-of-generative-causal","slug":"re-replication-study-of-generative-causal","title":"[Re] Replication Study of 'Generative causal explanations of black-box classifiers'","date":"2021-01-31","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/re-reproducing-learning-to-deceive-with","slug":"re-reproducing-learning-to-deceive-with","title":"[Re] Reproducing Learning to Deceive With Attention-Based Explanations","date":"2021-01-31","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/the-effect-of-differential-victim-crime","slug":"the-effect-of-differential-victim-crime","title":"The effect of differential victim crime reporting on predictive policing systems","date":"2021-01-30","arxiv_id":"2102.00128","repositories_listed":1,"syntology":null},{"url":"/paper/sampling-a-near-neighbor-in-high-dimensions","slug":"sampling-a-near-neighbor-in-high-dimensions","title":"Sampling a Near Neighbor in High Dimensions -- Who is the Fairest of Them All?","date":"2021-01-26","arxiv_id":"2101.10905","repositories_listed":1,"syntology":null},{"url":"/paper/through-the-data-management-lens-experimental","slug":"through-the-data-management-lens-experimental","title":"Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classification","date":"2021-01-18","arxiv_id":"2101.07361","repositories_listed":1,"syntology":null},{"url":"/paper/towards-long-term-fairness-in-recommendation","slug":"towards-long-term-fairness-in-recommendation","title":"Towards Long-term Fairness in Recommendation","date":"2021-01-10","arxiv_id":"2101.03584","repositories_listed":1,"syntology":null},{"url":"/paper/on-baselines-for-local-feature-attributions","slug":"on-baselines-for-local-feature-attributions","title":"On Baselines for Local Feature Attributions","date":"2021-01-04","arxiv_id":"2101.00905","repositories_listed":1,"syntology":null},{"url":"/paper/characterizing-fairness-over-the-set-of-good","slug":"characterizing-fairness-over-the-set-of-good","title":"Characterizing Fairness Over the Set of Good Models Under Selective Labels","date":"2021-01-02","arxiv_id":"2101.00352","repositories_listed":1,"syntology":null},{"url":"/paper/dimensions-of-transparency-in-nlp","slug":"dimensions-of-transparency-in-nlp","title":"Modeling Disclosive Transparency in NLP Application Descriptions","date":"2021-01-02","arxiv_id":"2101.00433","repositories_listed":1,"syntology":null},{"url":"/paper/on-dyadic-fairness-exploring-and-mitigating","slug":"on-dyadic-fairness-exploring-and-mitigating","title":"On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections","date":"2021-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/dalex-responsible-machine-learning-with","slug":"dalex-responsible-machine-learning-with","title":"dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python","date":"2020-12-28","arxiv_id":"2012.14406","repositories_listed":1,"syntology":null},{"url":"/paper/fairkit-fairkit-on-the-wall-who-s-the-fairest","slug":"fairkit-fairkit-on-the-wall-who-s-the-fairest","title":"Fairkit, Fairkit, on the Wall, Who's the Fairest of Them All? Supporting Data Scientists in Training Fair Models","date":"2020-12-17","arxiv_id":"2012.09951","repositories_listed":1,"syntology":null},{"url":"/paper/exacerbating-algorithmic-bias-through","slug":"exacerbating-algorithmic-bias-through","title":"Exacerbating Algorithmic Bias through Fairness Attacks","date":"2020-12-16","arxiv_id":"2012.08723","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/exacerbating-algorithmic-bias-through#ran","syntology_url":"https://syntology.ai/paper/2012.08723","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.08723"}},"official":{"repos":["Ninarehm/attack"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/varyfairyted-a-fair-in-rating-predictor-for","slug":"varyfairyted-a-fair-in-rating-predictor-for","title":"Fairness in Rating Prediction by Awareness of Verbal and Gesture Quality of Public Speeches","date":"2020-12-11","arxiv_id":"2012.06157","repositories_listed":1,"syntology":null},{"url":"/paper/repurposing-recidivism-models-for-forecasting","slug":"repurposing-recidivism-models-for-forecasting","title":"Repurposing recidivism models for forecasting police officer use of force","date":"2020-12-10","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/improving-the-fairness-of-deep-generative","slug":"improving-the-fairness-of-deep-generative","title":"Improving the Fairness of Deep Generative Models without Retraining","date":"2020-12-09","arxiv_id":"2012.04842","repositories_listed":1,"syntology":null},{"url":"/paper/fairod-fairness-aware-outlier-detection","slug":"fairod-fairness-aware-outlier-detection","title":"FairOD: Fairness-aware Outlier Detection","date":"2020-12-05","arxiv_id":"2012.03063","repositories_listed":1,"syntology":null},{"url":"/paper/machine-learning-for-public-policy-do-we-need","slug":"machine-learning-for-public-policy-do-we-need","title":"Empirical observation of negligible fairness-accuracy trade-offs in machine learning for public policy","date":"2020-12-05","arxiv_id":"2012.02972","repositories_listed":1,"syntology":null},{"url":"/paper/fairbatch-batch-selection-for-model-fairness-1","slug":"fairbatch-batch-selection-for-model-fairness-1","title":"FairBatch: Batch Selection for Model Fairness","date":"2020-12-03","arxiv_id":"2012.01696","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":1,"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/fairbatch-batch-selection-for-model-fairness-1#ran","syntology_url":"https://syntology.ai/paper/2012.01696","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.01696"}},"official":{"repos":["yuji-roh/fairbatch"],"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/reliable-model-compression-via-label","slug":"reliable-model-compression-via-label","title":"Going Beyond Classification Accuracy Metrics in Model Compression","date":"2020-12-03","arxiv_id":"2012.01604","repositories_listed":1,"syntology":null},{"url":"/paper/fair-attribute-classification-through-latent","slug":"fair-attribute-classification-through-latent","title":"Fair Attribute Classification through Latent Space De-biasing","date":"2020-12-02","arxiv_id":"2012.01469","repositories_listed":1,"syntology":null},{"url":"/paper/data-preprocessing-to-mitigate-bias-with","slug":"data-preprocessing-to-mitigate-bias-with","title":"Fair Densities via Boosting the Sufficient Statistics of Exponential Families","date":"2020-12-01","arxiv_id":"2012.00188","repositories_listed":1,"syntology":null},{"url":"/paper/fair-multiple-decision-making-through-soft","slug":"fair-multiple-decision-making-through-soft","title":"Fair Multiple Decision Making Through Soft Interventions","date":"2020-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/multimodal-review-generation-with-privacy-and","slug":"multimodal-review-generation-with-privacy-and","title":"Multimodal Review Generation with Privacy and Fairness Awareness","date":"2020-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/certified-monotonic-neural-networks-1","slug":"certified-monotonic-neural-networks-1","title":"Certified Monotonic Neural Networks","date":"2020-11-20","arxiv_id":"2011.10219","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/certified-monotonic-neural-networks-1#ran","syntology_url":"https://syntology.ai/paper/2011.10219","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.10219"}},"official":{"repos":["gnobitab/CertifiedMonotonicNetwork"],"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/exploring-text-specific-and-blackbox-fairness","slug":"exploring-text-specific-and-blackbox-fairness","title":"Exploring Text Specific and Blackbox Fairness Algorithms in Multimodal Clinical NLP","date":"2020-11-19","arxiv_id":"2011.09625","repositories_listed":1,"syntology":null},{"url":"/paper/fairness-and-robustness-in-invariant-learning","slug":"fairness-and-robustness-in-invariant-learning","title":"Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification","date":"2020-11-12","arxiv_id":"2011.06485","repositories_listed":1,"syntology":null},{"url":"/paper/two-sided-fairness-in-non-personalised","slug":"two-sided-fairness-in-non-personalised","title":"Two-Sided Fairness in Non-Personalised Recommendations","date":"2020-11-10","arxiv_id":"2011.05287","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-privacy-risks-of-algorithmic-fairness","slug":"on-the-privacy-risks-of-algorithmic-fairness","title":"On the Privacy Risks of Algorithmic Fairness","date":"2020-11-07","arxiv_id":"2011.03731","repositories_listed":1,"syntology":null},{"url":"/paper/convergent-algorithms-for-relaxed-minimax","slug":"convergent-algorithms-for-relaxed-minimax","title":"Minimax Group Fairness: Algorithms and Experiments","date":"2020-11-05","arxiv_id":"2011.03108","repositories_listed":1,"syntology":null},{"url":"/paper/minimax-pareto-fairness-a-multi-objective-1","slug":"minimax-pareto-fairness-a-multi-objective-1","title":"Minimax Pareto Fairness: A Multi Objective Perspective","date":"2020-11-03","arxiv_id":"2011.01821","repositories_listed":1,"syntology":null},{"url":"/paper/quadratic-metric-elicitation-with-application","slug":"quadratic-metric-elicitation-with-application","title":"Quadratic Metric Elicitation for Fairness and Beyond","date":"2020-11-03","arxiv_id":"2011.01516","repositories_listed":1,"syntology":null},{"url":"/paper/fair-classification-with-group-dependent","slug":"fair-classification-with-group-dependent","title":"Fair Classification with Group-Dependent Label Noise","date":"2020-10-31","arxiv_id":"2011.00379","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":3,"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: 0 honoured, 3 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fair-classification-with-group-dependent#ran","syntology_url":"https://syntology.ai/paper/2011.00379","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.00379"}},"official":{"repos":["Faldict/fair-classification-with-noisy-labels"],"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/thy-algorithm-shalt-not-bear-false-witness-an","slug":"thy-algorithm-shalt-not-bear-false-witness-an","title":"\"Thy algorithm shalt not bear false witness\": An Evaluation of Multiclass Debiasing Methods on Word Embeddings","date":"2020-10-30","arxiv_id":"2010.16228","repositories_listed":1,"syntology":null}],"record_sha256":"a92f71b2bfe30e34f1e495bf72cd939ba98a78d93d0be9063b4824290796bf36","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}