{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:D5SCYB7ZSSYFSXMXYUATDJWQ5U","short_pith_number":"pith:D5SCYB7Z","canonical_record":{"source":{"id":"1904.05233","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-10T15:10:37Z","cross_cats_sorted":["cs.CL","stat.ML"],"title_canon_sha256":"6a6b23c035b7155a0f233273d141f457f63cb5d3376f03a1c272144dac80625c","abstract_canon_sha256":"fa60fbccc840ebe375e08abc995ffb037ac0c39fd71a3d4718f7a2649f341e02"},"schema_version":"1.0"},"canonical_sha256":"1f642c07f994b0595d97c50131a6d0ed1dfac55c17e1922d0730caed97de6e52","source":{"kind":"arxiv","id":"1904.05233","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.05233","created_at":"2026-05-17T23:48:53Z"},{"alias_kind":"arxiv_version","alias_value":"1904.05233v1","created_at":"2026-05-17T23:48:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.05233","created_at":"2026-05-17T23:48:53Z"},{"alias_kind":"pith_short_12","alias_value":"D5SCYB7ZSSYF","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"D5SCYB7ZSSYFSXMX","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"D5SCYB7Z","created_at":"2026-05-18T12:33:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:D5SCYB7ZSSYFSXMXYUATDJWQ5U","target":"record","payload":{"canonical_record":{"source":{"id":"1904.05233","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-10T15:10:37Z","cross_cats_sorted":["cs.CL","stat.ML"],"title_canon_sha256":"6a6b23c035b7155a0f233273d141f457f63cb5d3376f03a1c272144dac80625c","abstract_canon_sha256":"fa60fbccc840ebe375e08abc995ffb037ac0c39fd71a3d4718f7a2649f341e02"},"schema_version":"1.0"},"canonical_sha256":"1f642c07f994b0595d97c50131a6d0ed1dfac55c17e1922d0730caed97de6e52","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:53.739804Z","signature_b64":"kHFSZTT0GKi50mkn8J4rU6OLK/cUSCvITTSvMbf9u9lK16GHe9k3rq9oT86Q2/+NtyvG0l1hfC3ZTHydvsEVDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1f642c07f994b0595d97c50131a6d0ed1dfac55c17e1922d0730caed97de6e52","last_reissued_at":"2026-05-17T23:48:53.739293Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:53.739293Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1904.05233","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":"2026-05-17T23:48:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZJwDt9agYArcIE88OGwKG/ATtykmi3R6VJu5yWCAlmBskWssEiUm7TjExrVJPC6eh6cCoIonup0P+jEVi8ZYBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T11:51:05.940247Z"},"content_sha256":"cf6cb049ee8d4df2b6ff076fd77445318c2aaef1119dc42da739ea05d9cc5601","schema_version":"1.0","event_id":"sha256:cf6cb049ee8d4df2b6ff076fd77445318c2aaef1119dc42da739ea05d9cc5601"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:D5SCYB7ZSSYFSXMXYUATDJWQ5U","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"What's in a Name? Reducing Bias in Bios without Access to Protected Attributes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","stat.ML"],"primary_cat":"cs.LG","authors_text":"Adam Tauman Kalai, Alexandra Chouldechova, Alexey Romanov, Anna Rumshisky, Christian Borgs, Hanna Wallach, Jennifer Chayes, Krishnaram Kenthapadi, Maria De-Arteaga, Sahin Geyik","submitted_at":"2019-04-10T15:10:37Z","abstract_excerpt":"There is a growing body of work that proposes methods for mitigating bias in machine learning systems. These methods typically rely on access to protected attributes such as race, gender, or age. However, this raises two significant challenges: (1) protected attributes may not be available or it may not be legal to use them, and (2) it is often desirable to simultaneously consider multiple protected attributes, as well as their intersections. In the context of mitigating bias in occupation classification, we propose a method for discouraging correlation between the predicted probability of an "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.05233","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":""},"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":"2026-05-17T23:48:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oxDhmDlmMy8wdawBzhIgU07j+anyLy2vuMrTE8x606mF0DLAT8a36qsd5XQRNAEqmh/fLP0aYoAABjGuyeT+Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T11:51:05.940607Z"},"content_sha256":"d709c3a1b168817753a5efa53cfad493959fadd71489e3eefa0a820dccb07558","schema_version":"1.0","event_id":"sha256:d709c3a1b168817753a5efa53cfad493959fadd71489e3eefa0a820dccb07558"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/D5SCYB7ZSSYFSXMXYUATDJWQ5U/bundle.json","state_url":"https://pith.science/pith/D5SCYB7ZSSYFSXMXYUATDJWQ5U/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/D5SCYB7ZSSYFSXMXYUATDJWQ5U/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":"2026-05-28T11:51:05Z","links":{"resolver":"https://pith.science/pith/D5SCYB7ZSSYFSXMXYUATDJWQ5U","bundle":"https://pith.science/pith/D5SCYB7ZSSYFSXMXYUATDJWQ5U/bundle.json","state":"https://pith.science/pith/D5SCYB7ZSSYFSXMXYUATDJWQ5U/state.json","well_known_bundle":"https://pith.science/.well-known/pith/D5SCYB7ZSSYFSXMXYUATDJWQ5U/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:D5SCYB7ZSSYFSXMXYUATDJWQ5U","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":"fa60fbccc840ebe375e08abc995ffb037ac0c39fd71a3d4718f7a2649f341e02","cross_cats_sorted":["cs.CL","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-10T15:10:37Z","title_canon_sha256":"6a6b23c035b7155a0f233273d141f457f63cb5d3376f03a1c272144dac80625c"},"schema_version":"1.0","source":{"id":"1904.05233","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.05233","created_at":"2026-05-17T23:48:53Z"},{"alias_kind":"arxiv_version","alias_value":"1904.05233v1","created_at":"2026-05-17T23:48:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.05233","created_at":"2026-05-17T23:48:53Z"},{"alias_kind":"pith_short_12","alias_value":"D5SCYB7ZSSYF","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"D5SCYB7ZSSYFSXMX","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"D5SCYB7Z","created_at":"2026-05-18T12:33:15Z"}],"graph_snapshots":[{"event_id":"sha256:d709c3a1b168817753a5efa53cfad493959fadd71489e3eefa0a820dccb07558","target":"graph","created_at":"2026-05-17T23:48:53Z","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"},"paper":{"abstract_excerpt":"There is a growing body of work that proposes methods for mitigating bias in machine learning systems. These methods typically rely on access to protected attributes such as race, gender, or age. However, this raises two significant challenges: (1) protected attributes may not be available or it may not be legal to use them, and (2) it is often desirable to simultaneously consider multiple protected attributes, as well as their intersections. In the context of mitigating bias in occupation classification, we propose a method for discouraging correlation between the predicted probability of an ","authors_text":"Adam Tauman Kalai, Alexandra Chouldechova, Alexey Romanov, Anna Rumshisky, Christian Borgs, Hanna Wallach, Jennifer Chayes, Krishnaram Kenthapadi, Maria De-Arteaga, Sahin Geyik","cross_cats":["cs.CL","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-10T15:10:37Z","title":"What's in a Name? Reducing Bias in Bios without Access to Protected Attributes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.05233","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:cf6cb049ee8d4df2b6ff076fd77445318c2aaef1119dc42da739ea05d9cc5601","target":"record","created_at":"2026-05-17T23:48:53Z","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":"fa60fbccc840ebe375e08abc995ffb037ac0c39fd71a3d4718f7a2649f341e02","cross_cats_sorted":["cs.CL","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-10T15:10:37Z","title_canon_sha256":"6a6b23c035b7155a0f233273d141f457f63cb5d3376f03a1c272144dac80625c"},"schema_version":"1.0","source":{"id":"1904.05233","kind":"arxiv","version":1}},"canonical_sha256":"1f642c07f994b0595d97c50131a6d0ed1dfac55c17e1922d0730caed97de6e52","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1f642c07f994b0595d97c50131a6d0ed1dfac55c17e1922d0730caed97de6e52","first_computed_at":"2026-05-17T23:48:53.739293Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:48:53.739293Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kHFSZTT0GKi50mkn8J4rU6OLK/cUSCvITTSvMbf9u9lK16GHe9k3rq9oT86Q2/+NtyvG0l1hfC3ZTHydvsEVDg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:48:53.739804Z","signed_message":"canonical_sha256_bytes"},"source_id":"1904.05233","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cf6cb049ee8d4df2b6ff076fd77445318c2aaef1119dc42da739ea05d9cc5601","sha256:d709c3a1b168817753a5efa53cfad493959fadd71489e3eefa0a820dccb07558"],"state_sha256":"742890974601540870dcab2b2d54ab363a6c9a09e1f9334847d1500a71fbbb21"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R9WJwAMt95triz3AOvszRtNZ36OQjq/R9DzSa0pR3+9DWYu/xyBdEqtmv7T6RrEldf2HkTPAqlCf3hdIYpz4Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T11:51:05.942451Z","bundle_sha256":"cf043271744df7de62c2c9f9262d590b96a60e488efe45664251aaf1fb5a90a9"}}