{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:MM3I6BWTEZTRDT2FUL3LVICBC3","short_pith_number":"pith:MM3I6BWT","canonical_record":{"source":{"id":"2106.12674","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-06-23T22:26:29Z","cross_cats_sorted":["cs.AI","cs.CY","stat.ML"],"title_canon_sha256":"f949f3c285abf0c5e0fc894464be242e1f31fb1d9b789f7d7bfe8ed8075cda1f","abstract_canon_sha256":"5617e85171ba9a7104eeab2cef0d3cfb9366753e60084216ee5e699ac308bf8a"},"schema_version":"1.0"},"canonical_sha256":"63368f06d3266711cf45a2f6baa04116f01db66d668b3611eaceaecf2cc631c8","source":{"kind":"arxiv","id":"2106.12674","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.12674","created_at":"2026-07-05T03:26:18Z"},{"alias_kind":"arxiv_version","alias_value":"2106.12674v2","created_at":"2026-07-05T03:26:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.12674","created_at":"2026-07-05T03:26:18Z"},{"alias_kind":"pith_short_12","alias_value":"MM3I6BWTEZTR","created_at":"2026-07-05T03:26:18Z"},{"alias_kind":"pith_short_16","alias_value":"MM3I6BWTEZTRDT2F","created_at":"2026-07-05T03:26:18Z"},{"alias_kind":"pith_short_8","alias_value":"MM3I6BWT","created_at":"2026-07-05T03:26:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:MM3I6BWTEZTRDT2FUL3LVICBC3","target":"record","payload":{"canonical_record":{"source":{"id":"2106.12674","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-06-23T22:26:29Z","cross_cats_sorted":["cs.AI","cs.CY","stat.ML"],"title_canon_sha256":"f949f3c285abf0c5e0fc894464be242e1f31fb1d9b789f7d7bfe8ed8075cda1f","abstract_canon_sha256":"5617e85171ba9a7104eeab2cef0d3cfb9366753e60084216ee5e699ac308bf8a"},"schema_version":"1.0"},"canonical_sha256":"63368f06d3266711cf45a2f6baa04116f01db66d668b3611eaceaecf2cc631c8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:26:18.795193Z","signature_b64":"X9/ePUSOc1/zlvQoXep/dkFHiugsQqTIEu0hPgerPVqvpczzhRnlASIpB31nDTUs+DfsJq8rDIwEEH5vQ7jnBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"63368f06d3266711cf45a2f6baa04116f01db66d668b3611eaceaecf2cc631c8","last_reissued_at":"2026-07-05T03:26:18.794772Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:26:18.794772Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2106.12674","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":"2026-07-05T03:26:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LW/KhVD/ICLhZLPX+T2wCaZ/E9A5ZWffw5QrH1IG0DeCCE6ICqs7ya8c+1CFgqjQK6Dr2nF/vJUaZhUOggGZDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T01:49:30.945021Z"},"content_sha256":"271e12c2ac905a790f82993477e55a9931d6ed2c40c98ce7c50711ffeecdd50c","schema_version":"1.0","event_id":"sha256:271e12c2ac905a790f82993477e55a9931d6ed2c40c98ce7c50711ffeecdd50c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:MM3I6BWTEZTRDT2FUL3LVICBC3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fairness via Representation Neutralization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CY","stat.ML"],"primary_cat":"cs.LG","authors_text":"Ahmed Hassan Awadallah, Guanchu Wang, Mengnan Du, Ruixiang Tang, Subhabrata Mukherjee, Xia Hu","submitted_at":"2021-06-23T22:26:29Z","abstract_excerpt":"Existing bias mitigation methods for DNN models primarily work on learning debiased encoders. This process not only requires a lot of instance-level annotations for sensitive attributes, it also does not guarantee that all fairness sensitive information has been removed from the encoder. To address these limitations, we explore the following research question: Can we reduce the discrimination of DNN models by only debiasing the classification head, even with biased representations as inputs? To this end, we propose a new mitigation technique, namely, Representation Neutralization for Fairness "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.12674","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/2106.12674/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":"2026-07-05T03:26:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CH7wICXAdxGVUf0aQ4mx+lAQyq1vkTT46bTFHGYPCS5ZVhDCscda/svgJTdqRmz1stACgV5xUEwDJVRrl7F7CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T01:49:30.946070Z"},"content_sha256":"8d3ac199dbc8e4a009e53e62c915fc3a0fddd1f37c7403ec1ee6f7424ce1caf4","schema_version":"1.0","event_id":"sha256:8d3ac199dbc8e4a009e53e62c915fc3a0fddd1f37c7403ec1ee6f7424ce1caf4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MM3I6BWTEZTRDT2FUL3LVICBC3/bundle.json","state_url":"https://pith.science/pith/MM3I6BWTEZTRDT2FUL3LVICBC3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MM3I6BWTEZTRDT2FUL3LVICBC3/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":"2026-08-11T01:49:30Z","links":{"resolver":"https://pith.science/pith/MM3I6BWTEZTRDT2FUL3LVICBC3","bundle":"https://pith.science/pith/MM3I6BWTEZTRDT2FUL3LVICBC3/bundle.json","state":"https://pith.science/pith/MM3I6BWTEZTRDT2FUL3LVICBC3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MM3I6BWTEZTRDT2FUL3LVICBC3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:MM3I6BWTEZTRDT2FUL3LVICBC3","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":"5617e85171ba9a7104eeab2cef0d3cfb9366753e60084216ee5e699ac308bf8a","cross_cats_sorted":["cs.AI","cs.CY","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-06-23T22:26:29Z","title_canon_sha256":"f949f3c285abf0c5e0fc894464be242e1f31fb1d9b789f7d7bfe8ed8075cda1f"},"schema_version":"1.0","source":{"id":"2106.12674","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.12674","created_at":"2026-07-05T03:26:18Z"},{"alias_kind":"arxiv_version","alias_value":"2106.12674v2","created_at":"2026-07-05T03:26:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.12674","created_at":"2026-07-05T03:26:18Z"},{"alias_kind":"pith_short_12","alias_value":"MM3I6BWTEZTR","created_at":"2026-07-05T03:26:18Z"},{"alias_kind":"pith_short_16","alias_value":"MM3I6BWTEZTRDT2F","created_at":"2026-07-05T03:26:18Z"},{"alias_kind":"pith_short_8","alias_value":"MM3I6BWT","created_at":"2026-07-05T03:26:18Z"}],"graph_snapshots":[{"event_id":"sha256:8d3ac199dbc8e4a009e53e62c915fc3a0fddd1f37c7403ec1ee6f7424ce1caf4","target":"graph","created_at":"2026-07-05T03:26:18Z","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/2106.12674/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Existing bias mitigation methods for DNN models primarily work on learning debiased encoders. This process not only requires a lot of instance-level annotations for sensitive attributes, it also does not guarantee that all fairness sensitive information has been removed from the encoder. To address these limitations, we explore the following research question: Can we reduce the discrimination of DNN models by only debiasing the classification head, even with biased representations as inputs? To this end, we propose a new mitigation technique, namely, Representation Neutralization for Fairness ","authors_text":"Ahmed Hassan Awadallah, Guanchu Wang, Mengnan Du, Ruixiang Tang, Subhabrata Mukherjee, Xia Hu","cross_cats":["cs.AI","cs.CY","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-06-23T22:26:29Z","title":"Fairness via Representation Neutralization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.12674","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":"sha256:271e12c2ac905a790f82993477e55a9931d6ed2c40c98ce7c50711ffeecdd50c","target":"record","created_at":"2026-07-05T03:26:18Z","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":"5617e85171ba9a7104eeab2cef0d3cfb9366753e60084216ee5e699ac308bf8a","cross_cats_sorted":["cs.AI","cs.CY","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-06-23T22:26:29Z","title_canon_sha256":"f949f3c285abf0c5e0fc894464be242e1f31fb1d9b789f7d7bfe8ed8075cda1f"},"schema_version":"1.0","source":{"id":"2106.12674","kind":"arxiv","version":2}},"canonical_sha256":"63368f06d3266711cf45a2f6baa04116f01db66d668b3611eaceaecf2cc631c8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"63368f06d3266711cf45a2f6baa04116f01db66d668b3611eaceaecf2cc631c8","first_computed_at":"2026-07-05T03:26:18.794772Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:26:18.794772Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"X9/ePUSOc1/zlvQoXep/dkFHiugsQqTIEu0hPgerPVqvpczzhRnlASIpB31nDTUs+DfsJq8rDIwEEH5vQ7jnBg==","signature_status":"signed_v1","signed_at":"2026-07-05T03:26:18.795193Z","signed_message":"canonical_sha256_bytes"},"source_id":"2106.12674","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:271e12c2ac905a790f82993477e55a9931d6ed2c40c98ce7c50711ffeecdd50c","sha256:8d3ac199dbc8e4a009e53e62c915fc3a0fddd1f37c7403ec1ee6f7424ce1caf4"],"state_sha256":"17d78e2bdf1a886bede85063f50cd356779b946a8bc88f09294ccd9f00c8f538"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hAQc7C2AnWUIK5YhsghsQCuoOHfc4pK4AP5/kvR29ibR82/Bij3F3jIKzUS/EYtMAu4CoCu2ZD4CH2L4bgNeBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T01:49:30.954488Z","bundle_sha256":"abda038b20eb7e5fce1aed8a44380b67fc83ebae5c0c8b67744c6a28afde570e"}}