{"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/2","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":2,"pages_in_order":57,"rows_per_page":100,"rows":[101,200],"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","next":"/task/fairness/papers/3","papers":[{"url":"/paper/packing-privacy-budget-efficiently","slug":"packing-privacy-budget-efficiently","title":"DPack: Efficiency-Oriented Privacy Budget Scheduling","date":"2022-12-26","arxiv_id":"2212.13228","repositories_listed":2,"syntology":null},{"url":"/paper/marginal-certainty-aware-fair-ranking","slug":"marginal-certainty-aware-fair-ranking","title":"Marginal-Certainty-aware Fair Ranking Algorithm","date":"2022-12-18","arxiv_id":"2212.09031","repositories_listed":2,"syntology":null},{"url":"/paper/robustness-disparities-in-face-detection","slug":"robustness-disparities-in-face-detection","title":"Robustness Disparities in Face Detection","date":"2022-11-29","arxiv_id":"2211.15937","repositories_listed":2,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/robustness-disparities-in-face-detection#ran","syntology_url":"https://syntology.ai/paper/2211.15937","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.15937"}},"official":{"repos":["dooleys/robustness-disparities-in-commercial-face-detection","dooleys/robustness"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/turning-the-tables-biased-imbalanced-dynamic","slug":"turning-the-tables-biased-imbalanced-dynamic","title":"Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation","date":"2022-11-24","arxiv_id":"2211.13358","repositories_listed":2,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/turning-the-tables-biased-imbalanced-dynamic#ran","syntology_url":"https://syntology.ai/paper/2211.13358","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.13358"}},"official":{"repos":["feedzai/bank-account-fraud"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/track2vec-fairness-music-recommendation-with","slug":"track2vec-fairness-music-recommendation-with","title":"Track2Vec: fairness music recommendation with a GPU-free customizable-driven framework","date":"2022-10-29","arxiv_id":"2210.16590","repositories_listed":2,"syntology":null},{"url":"/paper/mabel-attenuating-gender-bias-using-textual","slug":"mabel-attenuating-gender-bias-using-textual","title":"MABEL: Attenuating Gender Bias using Textual Entailment Data","date":"2022-10-26","arxiv_id":"2210.14975","repositories_listed":2,"syntology":{"n":6,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/mabel-attenuating-gender-bias-using-textual#ran","syntology_url":"https://syntology.ai/paper/2210.14975","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.14975"}},"official":{"repos":["princeton-nlp/mabel"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/item-based-variational-auto-encoder-for-fair","slug":"item-based-variational-auto-encoder-for-fair","title":"Item-based Variational Auto-encoder for Fair Music Recommendation","date":"2022-10-24","arxiv_id":"2211.01333","repositories_listed":2,"syntology":null},{"url":"/paper/rethinking-bias-mitigation-fairer","slug":"rethinking-bias-mitigation-fairer","title":"Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition","date":"2022-10-18","arxiv_id":"2210.09943","repositories_listed":2,"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/rethinking-bias-mitigation-fairer#ran","syntology_url":"https://syntology.ai/paper/2210.09943","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.09943"}},"official":{"repos":["dooleys/fr-nas"],"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/efficiently-computing-local-lipschitz","slug":"efficiently-computing-local-lipschitz","title":"Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation","date":"2022-10-13","arxiv_id":"2210.07394","repositories_listed":2,"syntology":{"n":25,"n_ran":14,"n_constructed":0,"n_ran_checked":8,"n_instrument":6,"n_unverified":11,"n_honours":2,"n_violates":2,"n_no_contract":4,"n_pointer_only":14,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 2 honoured, 2 violated, 4 with no contract checked; 6 where Syntology's instrument failed) · 11 unverified","sample_list":"/paper/efficiently-computing-local-lipschitz#ran","syntology_url":"https://syntology.ai/paper/2210.07394","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.07394"}},"official":{"repos":["shizhouxing/local-lipschitz-constants","verified-intelligence/auto_lirpa"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":11,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/medfair-benchmarking-fairness-for-medical","slug":"medfair-benchmarking-fairness-for-medical","title":"MEDFAIR: Benchmarking Fairness for Medical Imaging","date":"2022-10-04","arxiv_id":"2210.01725","repositories_listed":2,"syntology":null},{"url":"/paper/fairness-in-face-presentation-attack","slug":"fairness-in-face-presentation-attack","title":"Fairness in Face Presentation Attack Detection","date":"2022-09-19","arxiv_id":"2209.09035","repositories_listed":2,"syntology":null},{"url":"/paper/hidden-author-bias-in-book-recommendation","slug":"hidden-author-bias-in-book-recommendation","title":"Hidden Author Bias in Book Recommendation","date":"2022-09-01","arxiv_id":"2209.00371","repositories_listed":2,"syntology":null},{"url":"/paper/fairdisco-fairer-ai-in-dermatology-via","slug":"fairdisco-fairer-ai-in-dermatology-via","title":"FairDisCo: Fairer AI in Dermatology via Disentanglement Contrastive Learning","date":"2022-08-22","arxiv_id":"2208.10013","repositories_listed":2,"syntology":null},{"url":"/paper/minimax-auc-fairness-efficient-algorithm-with","slug":"minimax-auc-fairness-efficient-algorithm-with","title":"Minimax AUC Fairness: Efficient Algorithm with Provable Convergence","date":"2022-08-22","arxiv_id":"2208.10451","repositories_listed":2,"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/minimax-auc-fairness-efficient-algorithm-with#ran","syntology_url":"https://syntology.ai/paper/2208.10451","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.10451"}},"official":{"repos":["zhenhuan-yang/minimaxfairauc"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/an-approach-to-generalizing-some","slug":"an-approach-to-generalizing-some","title":"Impossibility theorems involving weakenings of expansion consistency and resoluteness in voting","date":"2022-08-14","arxiv_id":"2208.06907","repositories_listed":2,"syntology":null},{"url":"/paper/a-comprehensive-empirical-study-of-bias","slug":"a-comprehensive-empirical-study-of-bias","title":"A Comprehensive Empirical Study of Bias Mitigation Methods for Machine Learning Classifiers","date":"2022-07-07","arxiv_id":"2207.03277","repositories_listed":2,"syntology":null},{"url":"/paper/openxai-towards-a-transparent-evaluation-of","slug":"openxai-towards-a-transparent-evaluation-of","title":"OpenXAI: Towards a Transparent Evaluation of Model Explanations","date":"2022-06-22","arxiv_id":"2206.11104","repositories_listed":2,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/openxai-towards-a-transparent-evaluation-of#ran","syntology_url":"https://syntology.ai/paper/2206.11104","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.11104"}},"official":{"repos":["ai4life-group/openxai"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/motley-benchmarking-heterogeneity-and","slug":"motley-benchmarking-heterogeneity-and","title":"Motley: Benchmarking Heterogeneity and Personalization in Federated Learning","date":"2022-06-18","arxiv_id":"2206.09262","repositories_listed":2,"syntology":null},{"url":"/paper/recbole-2-0-towards-a-more-up-to-date","slug":"recbole-2-0-towards-a-more-up-to-date","title":"RecBole 2.0: Towards a More Up-to-Date Recommendation Library","date":"2022-06-15","arxiv_id":"2206.07351","repositories_listed":2,"syntology":null},{"url":"/paper/anchor-changing-regularized-natural-policy","slug":"anchor-changing-regularized-natural-policy","title":"Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning","date":"2022-06-10","arxiv_id":"2206.05357","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":2,"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; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/anchor-changing-regularized-natural-policy#ran","syntology_url":"https://syntology.ai/paper/2206.05357","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.05357"}},"official":{"repos":["tliu1997/arnpg-morl"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-justice-based-framework-for-the-analysis-of","slug":"a-justice-based-framework-for-the-analysis-of","title":"A Justice-Based Framework for the Analysis of Algorithmic Fairness-Utility Trade-Offs","date":"2022-06-06","arxiv_id":"2206.02891","repositories_listed":2,"syntology":null},{"url":"/paper/a-survey-on-fairness-for-machine-learning-on","slug":"a-survey-on-fairness-for-machine-learning-on","title":"A Survey on Fairness for Machine Learning on Graphs","date":"2022-05-11","arxiv_id":"2205.05396","repositories_listed":2,"syntology":null},{"url":"/paper/fairlib-a-unified-framework-for-assessing-and","slug":"fairlib-a-unified-framework-for-assessing-and","title":"fairlib: A Unified Framework for Assessing and Improving Classification Fairness","date":"2022-05-04","arxiv_id":"2205.01876","repositories_listed":2,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"5 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; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/fairlib-a-unified-framework-for-assessing-and#ran","syntology_url":"https://syntology.ai/paper/2205.01876","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.01876"}},"official":{"repos":["HanXudong/fairlib","hanxudong/fair_nlp_classification"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/joint-multisided-exposure-fairness-for","slug":"joint-multisided-exposure-fairness-for","title":"Joint Multisided Exposure Fairness for Recommendation","date":"2022-04-29","arxiv_id":"2205.00048","repositories_listed":2,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":2,"n_honours":4,"n_violates":1,"n_no_contract":0,"n_pointer_only":9,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 4 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/joint-multisided-exposure-fairness-for#ran","syntology_url":"https://syntology.ai/paper/2205.00048","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.00048"}},"official":{"repos":["haolun-wu/jmefairness"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/fairness-in-graph-mining-a-survey","slug":"fairness-in-graph-mining-a-survey","title":"Fairness in Graph Mining: A Survey","date":"2022-04-21","arxiv_id":"2204.09888","repositories_listed":2,"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":0,"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/fairness-in-graph-mining-a-survey#ran","syntology_url":"https://syntology.ai/paper/2204.09888","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.09888"}},"official":{"repos":["yushundong/graph-mining-fairness-data","yushundong/pygdebias"],"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/ethical-and-fairness-implications-of-model","slug":"ethical-and-fairness-implications-of-model","title":"Cross-model Fairness: Empirical Study of Fairness and Ethics Under Model Multiplicity","date":"2022-03-14","arxiv_id":"2203.07139","repositories_listed":2,"syntology":null},{"url":"/paper/mind-the-gap-understanding-the-modality-gap","slug":"mind-the-gap-understanding-the-modality-gap","title":"Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning","date":"2022-03-03","arxiv_id":"2203.02053","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mind-the-gap-understanding-the-modality-gap#ran","syntology_url":"https://syntology.ai/paper/2203.02053","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.02053"}},"official":{"repos":["weixin-liang/modality-gap"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/sampling-random-group-fair-rankings","slug":"sampling-random-group-fair-rankings","title":"Sampling Ex-Post Group-Fair Rankings","date":"2022-03-02","arxiv_id":"2203.00887","repositories_listed":2,"syntology":null},{"url":"/paper/the-unfairness-of-popularity-bias-in-book","slug":"the-unfairness-of-popularity-bias-in-book","title":"The Unfairness of Popularity Bias in Book Recommendation","date":"2022-02-27","arxiv_id":"2202.13446","repositories_listed":2,"syntology":null},{"url":"/paper/quantifying-memorization-across-neural","slug":"quantifying-memorization-across-neural","title":"Quantifying Memorization Across Neural Language Models","date":"2022-02-15","arxiv_id":"2202.07646","repositories_listed":2,"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/quantifying-memorization-across-neural#ran","syntology_url":"https://syntology.ai/paper/2202.07646","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.07646"}},"official":null}},{"url":"/paper/fairness-aware-configuration-of-machine","slug":"fairness-aware-configuration-of-machine","title":"Fairness-aware Configuration of Machine Learning Libraries","date":"2022-02-13","arxiv_id":"2202.06196","repositories_listed":2,"syntology":null},{"url":"/paper/fairness-implications-of-encoding-protected","slug":"fairness-implications-of-encoding-protected","title":"Fairness Implications of Encoding Protected Categorical Attributes","date":"2022-01-27","arxiv_id":"2201.11358","repositories_listed":2,"syntology":null},{"url":"/paper/personalized-federated-learning-through-local","slug":"personalized-federated-learning-through-local","title":"Personalized Federated Learning through Local Memorization","date":"2021-11-17","arxiv_id":"2111.09360","repositories_listed":2,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"0 ran · 2 unverified","sample_list":"/paper/personalized-federated-learning-through-local#ran","syntology_url":"https://syntology.ai/paper/2111.09360","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.09360"}},"official":{"repos":["omarfoq/knn-per"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/improving-fairness-via-federated-learning-1","slug":"improving-fairness-via-federated-learning-1","title":"Improving Fairness via Federated Learning","date":"2021-10-29","arxiv_id":"2110.15545","repositories_listed":2,"syntology":null},{"url":"/paper/fast-and-efficient-mmd-based-fair-pca-via","slug":"fast-and-efficient-mmd-based-fair-pca-via","title":"Fast and Efficient MMD-based Fair PCA via Optimization over Stiefel Manifold","date":"2021-09-23","arxiv_id":"2109.11196","repositories_listed":2,"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/fast-and-efficient-mmd-based-fair-pca-via#ran","syntology_url":"https://syntology.ai/paper/2109.11196","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.11196"}},"official":{"repos":["nick-jhlee/fair-manifold-pca"],"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/evaluating-fairness-in-argument-retrieval","slug":"evaluating-fairness-in-argument-retrieval","title":"Evaluating Fairness in Argument Retrieval","date":"2021-08-23","arxiv_id":"2108.10442","repositories_listed":2,"syntology":null},{"url":"/paper/sveva-fair-a-framework-for-evaluating","slug":"sveva-fair-a-framework-for-evaluating","title":"SVEva Fair: A Framework for Evaluating Fairness in Speaker Verification","date":"2021-07-26","arxiv_id":"2107.12049","repositories_listed":2,"syntology":null},{"url":"/paper/privacy-budget-scheduling","slug":"privacy-budget-scheduling","title":"Privacy Budget Scheduling","date":"2021-06-29","arxiv_id":"2106.15335","repositories_listed":2,"syntology":null},{"url":"/paper/multi-objective-asynchronous-successive","slug":"multi-objective-asynchronous-successive","title":"Multi-objective Asynchronous Successive Halving","date":"2021-06-23","arxiv_id":"2106.12639","repositories_listed":2,"syntology":null},{"url":"/paper/node-gam-neural-generalized-additive-model","slug":"node-gam-neural-generalized-additive-model","title":"NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning","date":"2021-06-03","arxiv_id":"2106.01613","repositories_listed":2,"syntology":{"n":15,"n_ran":9,"n_constructed":5,"n_ran_checked":8,"n_instrument":1,"n_unverified":6,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":3,"phrase":"9 ran (of which 5 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/node-gam-neural-generalized-additive-model#ran","syntology_url":"https://syntology.ai/paper/2106.01613","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.01613"}},"official":{"repos":["zzzace2000/nodegam"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":5,"n_ran_no_instrument_failure":6,"n_unverified":5,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/bias-in-machine-learning-software-why-how","slug":"bias-in-machine-learning-software-why-how","title":"Bias in Machine Learning Software: Why? How? What to do?","date":"2021-05-25","arxiv_id":"2105.12195","repositories_listed":2,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":5,"n_instrument":5,"n_unverified":1,"n_honours":5,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 5 honoured, 0 violated, 0 with no contract checked; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/bias-in-machine-learning-software-why-how#ran","syntology_url":"https://syntology.ai/paper/2105.12195","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.12195"}},"official":{"repos":["joymallyac/Fair-SMOTE"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/image-cropping-on-twitter-fairness-metrics","slug":"image-cropping-on-twitter-fairness-metrics","title":"Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency","date":"2021-05-18","arxiv_id":"2105.08667","repositories_listed":2,"syntology":null},{"url":"/paper/rtic-residual-learning-for-text-and-image","slug":"rtic-residual-learning-for-text-and-image","title":"RTIC: Residual Learning for Text and Image Composition using Graph Convolutional Network","date":"2021-04-07","arxiv_id":"2104.03015","repositories_listed":2,"syntology":null},{"url":"/paper/balancing-fairness-and-efficiency-in-traffic","slug":"balancing-fairness-and-efficiency-in-traffic","title":"Balancing Fairness and Efficiency in Traffic Routing via Interpolated Traffic Assignment","date":"2021-03-31","arxiv_id":"2104.00098","repositories_listed":2,"syntology":null},{"url":"/paper/promoting-fairness-through-hyperparameter","slug":"promoting-fairness-through-hyperparameter","title":"Promoting Fairness through Hyperparameter Optimization","date":"2021-03-23","arxiv_id":"2103.12715","repositories_listed":2,"syntology":null},{"url":"/paper/membership-inference-attacks-on-machine","slug":"membership-inference-attacks-on-machine","title":"Membership Inference Attacks on Machine Learning: A Survey","date":"2021-03-14","arxiv_id":"2103.07853","repositories_listed":2,"syntology":null},{"url":"/paper/debie-a-platform-for-implicit-and-explicit","slug":"debie-a-platform-for-implicit-and-explicit","title":"DebIE: A Platform for Implicit and Explicit Debiasing of Word Embedding Spaces","date":"2021-03-11","arxiv_id":"2103.06598","repositories_listed":2,"syntology":null},{"url":"/paper/fairness-on-the-ground-applying-algorithmic","slug":"fairness-on-the-ground-applying-algorithmic","title":"Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systems","date":"2021-03-10","arxiv_id":"2103.06172","repositories_listed":2,"syntology":null},{"url":"/paper/claimed-a-visual-and-scalable-component","slug":"claimed-a-visual-and-scalable-component","title":"CLAIMED, a visual and scalable component library for Trusted AI","date":"2021-03-04","arxiv_id":"2103.03281","repositories_listed":2,"syntology":null},{"url":"/paper/fjord-fair-and-accurate-federated-learning","slug":"fjord-fair-and-accurate-federated-learning","title":"FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout","date":"2021-02-26","arxiv_id":"2102.13451","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":2,"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 2 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; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/fjord-fair-and-accurate-federated-learning#ran","syntology_url":"https://syntology.ai/paper/2102.13451","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.13451"}},"official":{"repos":["adap/flower"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/challenges-in-automated-debiasing-for-toxic","slug":"challenges-in-automated-debiasing-for-toxic","title":"Challenges in Automated Debiasing for Toxic Language Detection","date":"2021-01-29","arxiv_id":"2102.00086","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/challenges-in-automated-debiasing-for-toxic#ran","syntology_url":"https://syntology.ai/paper/2102.00086","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.00086"}},"official":{"repos":["XuhuiZhou/Toxic_Debias"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/controllable-guarantees-for-fair-outcomes-via","slug":"controllable-guarantees-for-fair-outcomes-via","title":"Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation","date":"2021-01-11","arxiv_id":"2101.04108","repositories_listed":2,"syntology":{"n":6,"n_ran":5,"n_constructed":4,"n_ran_checked":4,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"5 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/controllable-guarantees-for-fair-outcomes-via#ran","syntology_url":"https://syntology.ai/paper/2101.04108","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.04108"}},"official":{"repos":["umgupta/fairness-via-contrastive-estimation"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/towards-building-a-robust-and-fair-federated","slug":"towards-building-a-robust-and-fair-federated","title":"A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning","date":"2020-11-20","arxiv_id":"2011.10464","repositories_listed":2,"syntology":null},{"url":"/paper/mitigating-bias-in-set-selection-with-noisy","slug":"mitigating-bias-in-set-selection-with-noisy","title":"Mitigating Bias in Set Selection with Noisy Protected Attributes","date":"2020-11-09","arxiv_id":"2011.04219","repositories_listed":2,"syntology":null},{"url":"/paper/to-be-robust-or-to-be-fair-towards-fairness-1","slug":"to-be-robust-or-to-be-fair-towards-fairness-1","title":"To be Robust or to be Fair: Towards Fairness in Adversarial Training","date":"2020-10-13","arxiv_id":"2010.06121","repositories_listed":2,"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":3,"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/to-be-robust-or-to-be-fair-towards-fairness-1#ran","syntology_url":"https://syntology.ai/paper/2010.06121","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.06121"}},"official":null}},{"url":"/paper/differentially-private-representation-for-nlp","slug":"differentially-private-representation-for-nlp","title":"Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness","date":"2020-10-03","arxiv_id":"2010.01285","repositories_listed":2,"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":1,"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/differentially-private-representation-for-nlp#ran","syntology_url":"https://syntology.ai/paper/2010.01285","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.01285"}},"official":{"repos":["xlhex/dpnlp"],"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/mars-gym-a-gym-framework-to-model-train-and","slug":"mars-gym-a-gym-framework-to-model-train-and","title":"MARS-Gym: A Gym framework to model, train, and evaluate Recommender Systems for Marketplaces","date":"2020-09-30","arxiv_id":"2010.07035","repositories_listed":2,"syntology":null},{"url":"/paper/ranking-for-individual-and-group-fairness","slug":"ranking-for-individual-and-group-fairness","title":"On the Problem of Underranking in Group-Fair Ranking","date":"2020-09-24","arxiv_id":"2010.06986","repositories_listed":2,"syntology":null},{"url":"/paper/fairness-matters-a-data-driven-framework","slug":"fairness-matters-a-data-driven-framework","title":"Fair and accurate age prediction using distribution aware data curation and augmentation","date":"2020-09-11","arxiv_id":"2009.05283","repositories_listed":2,"syntology":null},{"url":"/paper/neither-private-nor-fair-impact-of-data","slug":"neither-private-nor-fair-impact-of-data","title":"Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacy","date":"2020-09-10","arxiv_id":"2009.06389","repositories_listed":2,"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":1,"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/neither-private-nor-fair-impact-of-data#ran","syntology_url":"https://syntology.ai/paper/2009.06389","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.06389"}},"official":{"repos":["FarrandTom/deep-learning-fairness"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/fairgnn-eliminating-the-discrimination-in","slug":"fairgnn-eliminating-the-discrimination-in","title":"Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information","date":"2020-09-03","arxiv_id":"2009.01454","repositories_listed":2,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"0 ran · 2 unverified","sample_list":"/paper/fairgnn-eliminating-the-discrimination-in#ran","syntology_url":"https://syntology.ai/paper/2009.01454","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.01454"}},"official":{"repos":["EnyanDai/FairGNN"],"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/pollux-co-adaptive-cluster-scheduling-for","slug":"pollux-co-adaptive-cluster-scheduling-for","title":"Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning","date":"2020-08-27","arxiv_id":"2008.12260","repositories_listed":2,"syntology":null},{"url":"/paper/ensuring-fairness-beyond-the-training-data","slug":"ensuring-fairness-beyond-the-training-data","title":"Ensuring Fairness Beyond the Training Data","date":"2020-07-12","arxiv_id":"2007.06029","repositories_listed":2,"syntology":null},{"url":"/paper/attack-of-the-tails-yes-you-really-can","slug":"attack-of-the-tails-yes-you-really-can","title":"Attack of the Tails: Yes, You Really Can Backdoor Federated Learning","date":"2020-07-09","arxiv_id":"2007.05084","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/attack-of-the-tails-yes-you-really-can#ran","syntology_url":"https://syntology.ai/paper/2007.05084","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.05084"}},"official":{"repos":["ksreenivasan/OOD_Federated_Learning"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/tilted-empirical-risk-minimization","slug":"tilted-empirical-risk-minimization","title":"Tilted Empirical Risk Minimization","date":"2020-07-02","arxiv_id":"2007.01162","repositories_listed":2,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":5,"n_instrument":4,"n_unverified":1,"n_honours":1,"n_violates":4,"n_no_contract":0,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 4 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/tilted-empirical-risk-minimization#ran","syntology_url":"https://syntology.ai/paper/2007.01162","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.01162"}},"official":{"repos":["litian96/TERM"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/fedmgda-federated-learning-meets-multi","slug":"fedmgda-federated-learning-meets-multi","title":"Federated Learning Meets Multi-objective Optimization","date":"2020-06-20","arxiv_id":"2006.11489","repositories_listed":2,"syntology":null},{"url":"/paper/causal-intersectionality-for-fair-ranking","slug":"causal-intersectionality-for-fair-ranking","title":"Causal intersectionality for fair ranking","date":"2020-06-15","arxiv_id":"2006.08688","repositories_listed":2,"syntology":null},{"url":"/paper/is-independence-all-you-need-on-the","slug":"is-independence-all-you-need-on-the","title":"On Disentangled Representations Learned From Correlated Data","date":"2020-06-14","arxiv_id":"2006.07886","repositories_listed":2,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":9,"phrase":"8 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; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/is-independence-all-you-need-on-the#ran","syntology_url":"https://syntology.ai/paper/2006.07886","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.07886"}},"official":{"repos":["ftraeuble/disentanglement_lib"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-variational-approach-to-privacy-and","slug":"a-variational-approach-to-privacy-and","title":"A Variational Approach to Privacy and Fairness","date":"2020-06-11","arxiv_id":"2006.06332","repositories_listed":2,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/a-variational-approach-to-privacy-and#ran","syntology_url":"https://syntology.ai/paper/2006.06332","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.06332"}},"official":{"repos":["burklight/VariationalPrivacyFairness"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/do-the-machine-learning-models-on-a-crowd","slug":"do-the-machine-learning-models-on-a-crowd","title":"Do the Machine Learning Models on a Crowd Sourced Platform Exhibit Bias? An Empirical Study on Model Fairness","date":"2020-05-21","arxiv_id":"2005.12379","repositories_listed":2,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/do-the-machine-learning-models-on-a-crowd#ran","syntology_url":"https://syntology.ai/paper/2005.12379","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.12379"}},"official":{"repos":["sumonbis/ML-Fairness"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/null-it-out-guarding-protected-attributes-by","slug":"null-it-out-guarding-protected-attributes-by","title":"Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection","date":"2020-04-16","arxiv_id":"2004.07667","repositories_listed":2,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/null-it-out-guarding-protected-attributes-by#ran","syntology_url":"https://syntology.ai/paper/2004.07667","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.07667"}},"official":{"repos":["Shaul1321/nullspace_projection"],"state":"official: harvested for another paper","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":[]}}},{"url":"/paper/slice-tuner-a-selective-data-collection","slug":"slice-tuner-a-selective-data-collection","title":"Slice Tuner: A Selective Data Acquisition Framework for Accurate and Fair Machine Learning Models","date":"2020-03-10","arxiv_id":"2003.04549","repositories_listed":2,"syntology":null},{"url":"/paper/fairrec-two-sided-fairness-for-personalized","slug":"fairrec-two-sided-fairness-for-personalized","title":"FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms","date":"2020-02-25","arxiv_id":"2002.10764","repositories_listed":2,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":2,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/fairrec-two-sided-fairness-for-personalized#ran","syntology_url":"https://syntology.ai/paper/2002.10764","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.10764"}},"official":{"repos":["gourabkumarpatro/FairRec_www_2020"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/multilingual-twitter-corpus-and-baselines-for","slug":"multilingual-twitter-corpus-and-baselines-for","title":"Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition","date":"2020-02-24","arxiv_id":"2002.10361","repositories_listed":2,"syntology":{"n":13,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"9 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; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/multilingual-twitter-corpus-and-baselines-for#ran","syntology_url":"https://syntology.ai/paper/2002.10361","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.10361"}},"official":{"repos":["xiaoleihuang/Multilingual_Fairness_LREC"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/geometry-aware-generation-of-adversarial-and-1","slug":"geometry-aware-generation-of-adversarial-and-1","title":"Geometry-Aware Generation of Adversarial Point Clouds","date":"2019-12-24","arxiv_id":"1912.11171","repositories_listed":2,"syntology":null},{"url":"/paper/human-comprehension-of-fairness-in-machine","slug":"human-comprehension-of-fairness-in-machine","title":"Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics","date":"2019-12-17","arxiv_id":"2001.00089","repositories_listed":2,"syntology":null},{"url":"/paper/optimizing-generalized-rate-metrics-with","slug":"optimizing-generalized-rate-metrics-with","title":"Optimizing Generalized Rate Metrics with Three Players","date":"2019-12-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/maintaining-discrimination-and-fairness-in","slug":"maintaining-discrimination-and-fairness-in","title":"Maintaining Discrimination and Fairness in Class Incremental Learning","date":"2019-11-16","arxiv_id":"1911.07053","repositories_listed":2,"syntology":null},{"url":"/paper/selective-brain-damage-measuring-the","slug":"selective-brain-damage-measuring-the","title":"What Do Compressed Deep Neural Networks Forget?","date":"2019-11-13","arxiv_id":"1911.05248","repositories_listed":2,"syntology":null},{"url":"/paper/learning-fairness-in-multi-agent-systems","slug":"learning-fairness-in-multi-agent-systems","title":"Learning Fairness in Multi-Agent Systems","date":"2019-10-31","arxiv_id":"1910.14472","repositories_listed":2,"syntology":null},{"url":"/paper/a-survey-on-bias-and-fairness-in-machine","slug":"a-survey-on-bias-and-fairness-in-machine","title":"A Survey on Bias and Fairness in Machine Learning","date":"2019-08-23","arxiv_id":"1908.09635","repositories_listed":2,"syntology":null},{"url":"/paper/fairnas-rethinking-evaluation-fairness-of","slug":"fairnas-rethinking-evaluation-fairness-of","title":"FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search","date":"2019-07-03","arxiv_id":"1907.01845","repositories_listed":2,"syntology":null},{"url":"/paper/learning-fair-predictors-with-sensitive","slug":"learning-fair-predictors-with-sensitive","title":"Training individually fair ML models with Sensitive Subspace Robustness","date":"2019-06-28","arxiv_id":"1907.00020","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":2,"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; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-fair-predictors-with-sensitive#ran","syntology_url":"https://syntology.ai/paper/1907.00020","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.00020"}},"official":{"repos":["IBM/sensitive-subspace-robustness"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-fair-representations-for-kernel","slug":"learning-fair-representations-for-kernel","title":"Learning Fair Representations for Kernel Models","date":"2019-06-27","arxiv_id":"1906.11813","repositories_listed":2,"syntology":null},{"url":"/paper/clustering-with-fairness-constraints-a","slug":"clustering-with-fairness-constraints-a","title":"Variational Fair Clustering","date":"2019-06-19","arxiv_id":"1906.08207","repositories_listed":2,"syntology":null},{"url":"/paper/effectiveness-of-equalized-odds-for-fair","slug":"effectiveness-of-equalized-odds-for-fair","title":"Equalized odds postprocessing under imperfect group information","date":"2019-06-07","arxiv_id":"1906.03284","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"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) · 0 unverified","sample_list":"/paper/effectiveness-of-equalized-odds-for-fair#ran","syntology_url":"https://syntology.ai/paper/1906.03284","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.03284"}},"official":{"repos":["matthklein/equalized_odds_under_perturbation"],"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/190600389","slug":"190600389","title":"Disparate Vulnerability to Membership Inference Attacks","date":"2019-06-02","arxiv_id":"1906.00389","repositories_listed":2,"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/190600389#ran","syntology_url":"https://syntology.ai/paper/1906.00389","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.00389"}},"official":{"repos":["spring-epfl/disparate-vulnerability"],"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/fair-dimensionality-reduction-and-iterative","slug":"fair-dimensionality-reduction-and-iterative","title":"Multi-Criteria Dimensionality Reduction with Applications to Fairness","date":"2019-02-28","arxiv_id":"1902.11281","repositories_listed":2,"syntology":null},{"url":"/paper/pretending-fair-decisions-via-stealthily","slug":"pretending-fair-decisions-via-stealthily","title":"Faking Fairness via Stealthily Biased Sampling","date":"2019-01-24","arxiv_id":"1901.08291","repositories_listed":2,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 3 unverified","sample_list":"/paper/pretending-fair-decisions-via-stealthily#ran","syntology_url":"https://syntology.ai/paper/1901.08291","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.08291"}},"official":{"repos":["sato9hara/stealthily-biased-sampling"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"url":"/paper/fairwalk-towards-fair-graph-embedding","slug":"fairwalk-towards-fair-graph-embedding","title":"Fairwalk: Towards fair graph embedding","date":"2019-01-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/aequitas-a-bias-and-fairness-audit-toolkit","slug":"aequitas-a-bias-and-fairness-audit-toolkit","title":"Aequitas: A Bias and Fairness Audit Toolkit","date":"2018-11-14","arxiv_id":"1811.05577","repositories_listed":2,"syntology":null},{"url":"/paper/an-intersectional-definition-of-fairness","slug":"an-intersectional-definition-of-fairness","title":"An Intersectional Definition of Fairness","date":"2018-07-22","arxiv_id":"1807.08362","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 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; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/an-intersectional-definition-of-fairness#ran","syntology_url":"https://syntology.ai/paper/1807.08362","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.08362"}},"official":null}},{"url":"/paper/achieving-fairness-through-adversarial","slug":"achieving-fairness-through-adversarial","title":"Achieving Fairness through Adversarial Learning: an Application to Recidivism Prediction","date":"2018-06-30","arxiv_id":"1807.00199","repositories_listed":2,"syntology":null},{"url":"/paper/empirical-risk-minimization-under-fairness","slug":"empirical-risk-minimization-under-fairness","title":"Empirical Risk Minimization under Fairness Constraints","date":"2018-02-23","arxiv_id":"1802.08626","repositories_listed":2,"syntology":null},{"url":"/paper/convex-formulations-for-fair-principal","slug":"convex-formulations-for-fair-principal","title":"Convex Formulations for Fair Principal Component Analysis","date":"2018-02-11","arxiv_id":"1802.03765","repositories_listed":2,"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/convex-formulations-for-fair-principal#ran","syntology_url":"https://syntology.ai/paper/1802.03765","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.03765"}},"official":{"repos":["molfat66/FairML"],"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/calibration-for-the-computationally","slug":"calibration-for-the-computationally","title":"Calibration for the (Computationally-Identifiable) Masses","date":"2017-11-22","arxiv_id":"1711.08513","repositories_listed":2,"syntology":null},{"url":"/paper/penalizing-unfairness-in-binary","slug":"penalizing-unfairness-in-binary","title":"Penalizing Unfairness in Binary Classification","date":"2017-06-30","arxiv_id":"1707.00044","repositories_listed":2,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/penalizing-unfairness-in-binary#ran","syntology_url":"https://syntology.ai/paper/1707.00044","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1707.00044"}},"official":{"repos":["jjgold012/lab-project-fairness"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/ranking-with-fairness-constraints","slug":"ranking-with-fairness-constraints","title":"Ranking with Fairness Constraints","date":"2017-04-22","arxiv_id":"1704.06840","repositories_listed":2,"syntology":null},{"url":"/paper/on-the-impossibility-of-fairness","slug":"on-the-impossibility-of-fairness","title":"On the (im)possibility of fairness","date":"2016-09-23","arxiv_id":"1609.07236","repositories_listed":2,"syntology":null},{"url":"/paper/fairness-constraints-mechanisms-for-fair","slug":"fairness-constraints-mechanisms-for-fair","title":"Fairness Constraints: Mechanisms for Fair Classification","date":"2015-07-19","arxiv_id":"1507.05259","repositories_listed":2,"syntology":null}],"record_sha256":"0947ef096286f814048d71867cd925f41829796d10bf0d27ebbae29141d207f2","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}