{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:KI4TBA2O6CXZICPZC2LFENQ2ZS","short_pith_number":"pith:KI4TBA2O","canonical_record":{"source":{"id":"2109.09447","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2021-09-20T12:05:14Z","cross_cats_sorted":["cs.AI","cs.CY","stat.AP"],"title_canon_sha256":"9711e41f56f5fb09481321de1b478e5bc2f02d2a34742a5008e0c47865ef1e23","abstract_canon_sha256":"69d1499deaa8951422f641df4ee187bec7fdf6f3a60f7b82f359b881f068ab58"},"schema_version":"1.0"},"canonical_sha256":"523930834ef0af9409f9169652361accb88e3f2301e681f577c51e79bb8fe956","source":{"kind":"arxiv","id":"2109.09447","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.09447","created_at":"2026-07-05T04:28:07Z"},{"alias_kind":"arxiv_version","alias_value":"2109.09447v2","created_at":"2026-07-05T04:28:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.09447","created_at":"2026-07-05T04:28:07Z"},{"alias_kind":"pith_short_12","alias_value":"KI4TBA2O6CXZ","created_at":"2026-07-05T04:28:07Z"},{"alias_kind":"pith_short_16","alias_value":"KI4TBA2O6CXZICPZ","created_at":"2026-07-05T04:28:07Z"},{"alias_kind":"pith_short_8","alias_value":"KI4TBA2O","created_at":"2026-07-05T04:28:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:KI4TBA2O6CXZICPZC2LFENQ2ZS","target":"record","payload":{"canonical_record":{"source":{"id":"2109.09447","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2021-09-20T12:05:14Z","cross_cats_sorted":["cs.AI","cs.CY","stat.AP"],"title_canon_sha256":"9711e41f56f5fb09481321de1b478e5bc2f02d2a34742a5008e0c47865ef1e23","abstract_canon_sha256":"69d1499deaa8951422f641df4ee187bec7fdf6f3a60f7b82f359b881f068ab58"},"schema_version":"1.0"},"canonical_sha256":"523930834ef0af9409f9169652361accb88e3f2301e681f577c51e79bb8fe956","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:28:07.819465Z","signature_b64":"z+DGmr8GwijQInoU4tPe3UjV+P0tEGO+ZuiXWBlqWyFKW8IHRowfH1jz51JebBt7sAHuaaVX0Ike6eEgml/jDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"523930834ef0af9409f9169652361accb88e3f2301e681f577c51e79bb8fe956","last_reissued_at":"2026-07-05T04:28:07.818904Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:28:07.818904Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2109.09447","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:28:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1SLubLeKHVX40Nya4S2tVZ/Vghtn439axbo/Kfiwxs7QKHD6GJeAi0PAtS1fWexIyw9zmKNGPLmABNZqrg16AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T13:53:30.212828Z"},"content_sha256":"088850b499212587ee5f7d36aaa4122a3d11cf444e6357dd4208e2618cc932c6","schema_version":"1.0","event_id":"sha256:088850b499212587ee5f7d36aaa4122a3d11cf444e6357dd4208e2618cc932c6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:KI4TBA2O6CXZICPZC2LFENQ2ZS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Algorithmic Fairness Verification with Graphical Models","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CY","stat.AP"],"primary_cat":"cs.LG","authors_text":"Bishwamittra Ghosh, Debabrota Basu, Kuldeep S. Meel","submitted_at":"2021-09-20T12:05:14Z","abstract_excerpt":"In recent years, machine learning (ML) algorithms have been deployed in safety-critical and high-stake decision-making, where the fairness of algorithms is of paramount importance. Fairness in ML centers on detecting bias towards certain demographic populations induced by an ML classifier and proposes algorithmic solutions to mitigate the bias with respect to different fairness definitions. To this end, several fairness verifiers have been proposed that compute the bias in the prediction of an ML classifier--essentially beyond a finite dataset--given the probability distribution of input featu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.09447","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2109.09447/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:28:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"j7V0z6pWAcnX89W2B/Oi8XAS/MCapxH6ccy8nsEh/XuKrQ4g1xKHPl0woJHx1TjcgKRrcHZTx8iV8jv9nY7QCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T13:53:30.213465Z"},"content_sha256":"a1133d9a87df5ce83b5d1a8afaec45c4a8fc1e85094bd7a44d27263afc01dbd0","schema_version":"1.0","event_id":"sha256:a1133d9a87df5ce83b5d1a8afaec45c4a8fc1e85094bd7a44d27263afc01dbd0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KI4TBA2O6CXZICPZC2LFENQ2ZS/bundle.json","state_url":"https://pith.science/pith/KI4TBA2O6CXZICPZC2LFENQ2ZS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KI4TBA2O6CXZICPZC2LFENQ2ZS/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T13:53:30Z","links":{"resolver":"https://pith.science/pith/KI4TBA2O6CXZICPZC2LFENQ2ZS","bundle":"https://pith.science/pith/KI4TBA2O6CXZICPZC2LFENQ2ZS/bundle.json","state":"https://pith.science/pith/KI4TBA2O6CXZICPZC2LFENQ2ZS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KI4TBA2O6CXZICPZC2LFENQ2ZS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:KI4TBA2O6CXZICPZC2LFENQ2ZS","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"69d1499deaa8951422f641df4ee187bec7fdf6f3a60f7b82f359b881f068ab58","cross_cats_sorted":["cs.AI","cs.CY","stat.AP"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2021-09-20T12:05:14Z","title_canon_sha256":"9711e41f56f5fb09481321de1b478e5bc2f02d2a34742a5008e0c47865ef1e23"},"schema_version":"1.0","source":{"id":"2109.09447","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.09447","created_at":"2026-07-05T04:28:07Z"},{"alias_kind":"arxiv_version","alias_value":"2109.09447v2","created_at":"2026-07-05T04:28:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.09447","created_at":"2026-07-05T04:28:07Z"},{"alias_kind":"pith_short_12","alias_value":"KI4TBA2O6CXZ","created_at":"2026-07-05T04:28:07Z"},{"alias_kind":"pith_short_16","alias_value":"KI4TBA2O6CXZICPZ","created_at":"2026-07-05T04:28:07Z"},{"alias_kind":"pith_short_8","alias_value":"KI4TBA2O","created_at":"2026-07-05T04:28:07Z"}],"graph_snapshots":[{"event_id":"sha256:a1133d9a87df5ce83b5d1a8afaec45c4a8fc1e85094bd7a44d27263afc01dbd0","target":"graph","created_at":"2026-07-05T04:28:07Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2109.09447/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In recent years, machine learning (ML) algorithms have been deployed in safety-critical and high-stake decision-making, where the fairness of algorithms is of paramount importance. Fairness in ML centers on detecting bias towards certain demographic populations induced by an ML classifier and proposes algorithmic solutions to mitigate the bias with respect to different fairness definitions. To this end, several fairness verifiers have been proposed that compute the bias in the prediction of an ML classifier--essentially beyond a finite dataset--given the probability distribution of input featu","authors_text":"Bishwamittra Ghosh, Debabrota Basu, Kuldeep S. Meel","cross_cats":["cs.AI","cs.CY","stat.AP"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2021-09-20T12:05:14Z","title":"Algorithmic Fairness Verification with Graphical Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.09447","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:088850b499212587ee5f7d36aaa4122a3d11cf444e6357dd4208e2618cc932c6","target":"record","created_at":"2026-07-05T04:28:07Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"69d1499deaa8951422f641df4ee187bec7fdf6f3a60f7b82f359b881f068ab58","cross_cats_sorted":["cs.AI","cs.CY","stat.AP"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2021-09-20T12:05:14Z","title_canon_sha256":"9711e41f56f5fb09481321de1b478e5bc2f02d2a34742a5008e0c47865ef1e23"},"schema_version":"1.0","source":{"id":"2109.09447","kind":"arxiv","version":2}},"canonical_sha256":"523930834ef0af9409f9169652361accb88e3f2301e681f577c51e79bb8fe956","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"523930834ef0af9409f9169652361accb88e3f2301e681f577c51e79bb8fe956","first_computed_at":"2026-07-05T04:28:07.818904Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:28:07.818904Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"z+DGmr8GwijQInoU4tPe3UjV+P0tEGO+ZuiXWBlqWyFKW8IHRowfH1jz51JebBt7sAHuaaVX0Ike6eEgml/jDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:28:07.819465Z","signed_message":"canonical_sha256_bytes"},"source_id":"2109.09447","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:088850b499212587ee5f7d36aaa4122a3d11cf444e6357dd4208e2618cc932c6","sha256:a1133d9a87df5ce83b5d1a8afaec45c4a8fc1e85094bd7a44d27263afc01dbd0"],"state_sha256":"205216d1c6ba51704c1461bb1de096d123694c9ff48b48d3c30ae06cdd5bc31a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qbrNIgkikMC/RFaezY3wCwvG1Mmy6NM/a6DnsWpYH2eKXN3sJk8fZJvhAxz2Z0xyyUtZa/efA2fdtsObQYQuCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T13:53:30.217642Z","bundle_sha256":"3a122b25623003ce8bbbb27b2794811648da4260d17fa5857220cd83fd259c4a"}}