{"about":{"site":"https://codewithpapers.app","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.","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"},"url":"/paper/measuring-fairness-in-generative-models","title":"Measuring Fairness in Generative Models","arxiv_id":"2107.07754","date":"2021-07-16","proceeding":null,"authors":["Christopher T. H Teo","Ngai-Man Cheung"],"abstract":"Deep generative models have made much progress in improving training stability and quality of generated data. Recently there has been increased interest in the fairness of deep-generated data. Fairness is important in many applications, e.g. law enforcement, as biases will affect efficacy. Central to fair data generation are the fairness metrics for the assessment and evaluation of different generative models. In this paper, we first review fairness metrics proposed in previous works and highlight potential weaknesses. We then discuss a performance benchmark framework along with the assessment of alternative metrics.","url_abs":"https://arxiv.org/abs/2107.07754v1","url_pdf":"https://arxiv.org/pdf/2107.07754v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"measuring-fairness-in-generative-models","repo_url":"https://github.com/Bearwithchris/Fairness_Metric","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"fairness","task_name":"Fairness"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2107.07754","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.07754"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Bearwithchris/Fairness_Metric","reach":null}],"summary":{"ran_fixture":1,"ran_honours":1,"ran_draft_wrong":1,"unverified":1},"by_repo_kind":{"official":{"samples":4,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":4,"samples":[{"code_sha256_prefix":"6fa290287d576e29","entry":"classify_examples","repo":"Bearwithchris/Fairness_Metric","repo_kind":"official","path":"Testing_metrics/sample_test.py","file_url":"https://github.com/Bearwithchris/Fairness_Metric/blob/HEAD/Testing_metrics/sample_test.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"6fa290287d576e29"}},{"code_sha256_prefix":"b0b12a75d3201590","entry":"load_attributes","repo":"Bearwithchris/Fairness_Metric","repo_kind":"official","path":"Data_prep/preprocess_celeba.py","file_url":"https://github.com/Bearwithchris/Fairness_Metric/blob/HEAD/Data_prep/preprocess_celeba.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"b0b12a75d3201590"}},{"code_sha256_prefix":"115ca35fe166b65c","entry":"load_eval_partition","repo":"Bearwithchris/Fairness_Metric","repo_kind":"official","path":"Data_prep/preprocess_celeba.py","file_url":"https://github.com/Bearwithchris/Fairness_Metric/blob/HEAD/Data_prep/preprocess_celeba.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"115ca35fe166b65c"}},{"code_sha256_prefix":"ad8bf509c9012d8a","entry":"load_data","repo":"Bearwithchris/Fairness_Metric","repo_kind":"official","path":"Testing_metrics/sample_test.py","file_url":"https://github.com/Bearwithchris/Fairness_Metric/blob/HEAD/Testing_metrics/sample_test.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"ad8bf509c9012d8a"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}