{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:HWWPMDN5PWWUUMJ6JXWE3ZGOVO","short_pith_number":"pith:HWWPMDN5","canonical_record":{"source":{"id":"2009.12040","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-09-25T05:48:56Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"8dd7be816042c0fa7382b3d568c1b54cb78b09bf5c4c10f941f4b499a0160cb5","abstract_canon_sha256":"33cf1d1468dcda30e80cd622d90ac4e4dc5bc8011181b2bfecc8167cfe8b5465"},"schema_version":"1.0"},"canonical_sha256":"3dacf60dbd7dad4a313e4dec4de4ceabb1391379eabab0d04ca5ea231dea4dcc","source":{"kind":"arxiv","id":"2009.12040","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2009.12040","created_at":"2026-07-05T01:37:58Z"},{"alias_kind":"arxiv_version","alias_value":"2009.12040v1","created_at":"2026-07-05T01:37:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.12040","created_at":"2026-07-05T01:37:58Z"},{"alias_kind":"pith_short_12","alias_value":"HWWPMDN5PWWU","created_at":"2026-07-05T01:37:58Z"},{"alias_kind":"pith_short_16","alias_value":"HWWPMDN5PWWUUMJ6","created_at":"2026-07-05T01:37:58Z"},{"alias_kind":"pith_short_8","alias_value":"HWWPMDN5","created_at":"2026-07-05T01:37:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:HWWPMDN5PWWUUMJ6JXWE3ZGOVO","target":"record","payload":{"canonical_record":{"source":{"id":"2009.12040","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-09-25T05:48:56Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"8dd7be816042c0fa7382b3d568c1b54cb78b09bf5c4c10f941f4b499a0160cb5","abstract_canon_sha256":"33cf1d1468dcda30e80cd622d90ac4e4dc5bc8011181b2bfecc8167cfe8b5465"},"schema_version":"1.0"},"canonical_sha256":"3dacf60dbd7dad4a313e4dec4de4ceabb1391379eabab0d04ca5ea231dea4dcc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:37:58.937735Z","signature_b64":"R0Sx8rNPUdUJ12sTobcx770ftE9WFYrBcs8/bDUzlYAqLr0trJ6hoMoAW4UgeO021n4RXGpQ1z1yvTI2/EkHDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3dacf60dbd7dad4a313e4dec4de4ceabb1391379eabab0d04ca5ea231dea4dcc","last_reissued_at":"2026-07-05T01:37:58.937388Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:37:58.937388Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2009.12040","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:37:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IYkxcvfsmtQjsrD0soqS/VJDUHdgatmXQG6MJFxRXU1jo61Tdw0s+pHTpCMVgh1kdxz3gMy8KTCbL4QqYPUtBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T12:37:31.339006Z"},"content_sha256":"e53592719b660d4d4e4ed99814e1d414806f80a44a7997775a7aaac6f837c418","schema_version":"1.0","event_id":"sha256:e53592719b660d4d4e4ed99814e1d414806f80a44a7997775a7aaac6f837c418"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:HWWPMDN5PWWUUMJ6JXWE3ZGOVO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce Discrimination","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Jing Li, Mengde Han, Philip S. Yu, Tao Zhang, Tianqing Zhu, Wanlei Zhou","submitted_at":"2020-09-25T05:48:56Z","abstract_excerpt":"A growing specter in the rise of machine learning is whether the decisions made by machine learning models are fair. While research is already underway to formalize a machine-learning concept of fairness and to design frameworks for building fair models with sacrifice in accuracy, most are geared toward either supervised or unsupervised learning. Yet two observations inspired us to wonder whether semi-supervised learning might be useful to solve discrimination problems. First, previous study showed that increasing the size of the training set may lead to a better trade-off between fairness and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.12040","kind":"arxiv","version":1},"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/2009.12040/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":"2026-07-05T01:37:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PUDl6CHUXSe4G81Qg1Y/NeS7AsSxqWjx4F+/pzHBjEWlmduwfguHxntcaVwMt3qn9me3DKMFO4kwEhes8/tXBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T12:37:31.339519Z"},"content_sha256":"34834b046abb88350638ff0faa5522e676bc98f1b7264b6cad026e679813efce","schema_version":"1.0","event_id":"sha256:34834b046abb88350638ff0faa5522e676bc98f1b7264b6cad026e679813efce"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HWWPMDN5PWWUUMJ6JXWE3ZGOVO/bundle.json","state_url":"https://pith.science/pith/HWWPMDN5PWWUUMJ6JXWE3ZGOVO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HWWPMDN5PWWUUMJ6JXWE3ZGOVO/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":"2026-08-05T12:37:31Z","links":{"resolver":"https://pith.science/pith/HWWPMDN5PWWUUMJ6JXWE3ZGOVO","bundle":"https://pith.science/pith/HWWPMDN5PWWUUMJ6JXWE3ZGOVO/bundle.json","state":"https://pith.science/pith/HWWPMDN5PWWUUMJ6JXWE3ZGOVO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HWWPMDN5PWWUUMJ6JXWE3ZGOVO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:HWWPMDN5PWWUUMJ6JXWE3ZGOVO","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":"33cf1d1468dcda30e80cd622d90ac4e4dc5bc8011181b2bfecc8167cfe8b5465","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-09-25T05:48:56Z","title_canon_sha256":"8dd7be816042c0fa7382b3d568c1b54cb78b09bf5c4c10f941f4b499a0160cb5"},"schema_version":"1.0","source":{"id":"2009.12040","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2009.12040","created_at":"2026-07-05T01:37:58Z"},{"alias_kind":"arxiv_version","alias_value":"2009.12040v1","created_at":"2026-07-05T01:37:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.12040","created_at":"2026-07-05T01:37:58Z"},{"alias_kind":"pith_short_12","alias_value":"HWWPMDN5PWWU","created_at":"2026-07-05T01:37:58Z"},{"alias_kind":"pith_short_16","alias_value":"HWWPMDN5PWWUUMJ6","created_at":"2026-07-05T01:37:58Z"},{"alias_kind":"pith_short_8","alias_value":"HWWPMDN5","created_at":"2026-07-05T01:37:58Z"}],"graph_snapshots":[{"event_id":"sha256:34834b046abb88350638ff0faa5522e676bc98f1b7264b6cad026e679813efce","target":"graph","created_at":"2026-07-05T01:37:58Z","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/2009.12040/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"A growing specter in the rise of machine learning is whether the decisions made by machine learning models are fair. While research is already underway to formalize a machine-learning concept of fairness and to design frameworks for building fair models with sacrifice in accuracy, most are geared toward either supervised or unsupervised learning. Yet two observations inspired us to wonder whether semi-supervised learning might be useful to solve discrimination problems. First, previous study showed that increasing the size of the training set may lead to a better trade-off between fairness and","authors_text":"Jing Li, Mengde Han, Philip S. Yu, Tao Zhang, Tianqing Zhu, Wanlei Zhou","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-09-25T05:48:56Z","title":"Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce Discrimination"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.12040","kind":"arxiv","version":1},"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:e53592719b660d4d4e4ed99814e1d414806f80a44a7997775a7aaac6f837c418","target":"record","created_at":"2026-07-05T01:37:58Z","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":"33cf1d1468dcda30e80cd622d90ac4e4dc5bc8011181b2bfecc8167cfe8b5465","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-09-25T05:48:56Z","title_canon_sha256":"8dd7be816042c0fa7382b3d568c1b54cb78b09bf5c4c10f941f4b499a0160cb5"},"schema_version":"1.0","source":{"id":"2009.12040","kind":"arxiv","version":1}},"canonical_sha256":"3dacf60dbd7dad4a313e4dec4de4ceabb1391379eabab0d04ca5ea231dea4dcc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3dacf60dbd7dad4a313e4dec4de4ceabb1391379eabab0d04ca5ea231dea4dcc","first_computed_at":"2026-07-05T01:37:58.937388Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:37:58.937388Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"R0Sx8rNPUdUJ12sTobcx770ftE9WFYrBcs8/bDUzlYAqLr0trJ6hoMoAW4UgeO021n4RXGpQ1z1yvTI2/EkHDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T01:37:58.937735Z","signed_message":"canonical_sha256_bytes"},"source_id":"2009.12040","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e53592719b660d4d4e4ed99814e1d414806f80a44a7997775a7aaac6f837c418","sha256:34834b046abb88350638ff0faa5522e676bc98f1b7264b6cad026e679813efce"],"state_sha256":"d663725c9bbeb660647347fc89477b664ed7b7b9e8d26af7db137ae48f9235d8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3d1HjfQq8uWL4uv/ufRM1rKPePIii+w0wXzIHCWcxP6CZhF5obCSz3d8y7ugE1Bny7c3t87ZeBpOuZNevOM5Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T12:37:31.343820Z","bundle_sha256":"5c0e9779641cadbb16a87abc9209ea08e25f1d0c0c3ea5e315d001ce79b8c462"}}