{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:J3XB5BQ2NXSY3DCNVD7LNUUHMZ","short_pith_number":"pith:J3XB5BQ2","canonical_record":{"source":{"id":"1601.05764","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-01-21T19:48:07Z","cross_cats_sorted":["cs.CY"],"title_canon_sha256":"f3cbc42466f6db2452721c83398c57317410911dd859cd3812479c403b615c8c","abstract_canon_sha256":"9ec6443b33df5bcd6e605892b73f1e39fb4d56fffe11de3dbc2d717c318811ce"},"schema_version":"1.0"},"canonical_sha256":"4eee1e861a6de58d8c4da8feb6d287665a0a2743d91bf74d1f7a8f74a83c2e7d","source":{"kind":"arxiv","id":"1601.05764","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1601.05764","created_at":"2026-05-18T01:22:13Z"},{"alias_kind":"arxiv_version","alias_value":"1601.05764v1","created_at":"2026-05-18T01:22:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1601.05764","created_at":"2026-05-18T01:22:13Z"},{"alias_kind":"pith_short_12","alias_value":"J3XB5BQ2NXSY","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_16","alias_value":"J3XB5BQ2NXSY3DCN","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_8","alias_value":"J3XB5BQ2","created_at":"2026-05-18T12:30:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:J3XB5BQ2NXSY3DCNVD7LNUUHMZ","target":"record","payload":{"canonical_record":{"source":{"id":"1601.05764","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-01-21T19:48:07Z","cross_cats_sorted":["cs.CY"],"title_canon_sha256":"f3cbc42466f6db2452721c83398c57317410911dd859cd3812479c403b615c8c","abstract_canon_sha256":"9ec6443b33df5bcd6e605892b73f1e39fb4d56fffe11de3dbc2d717c318811ce"},"schema_version":"1.0"},"canonical_sha256":"4eee1e861a6de58d8c4da8feb6d287665a0a2743d91bf74d1f7a8f74a83c2e7d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:22:13.022277Z","signature_b64":"zrl/ekvMhbg2X6zm6PmtLFtX7WonEplgAA+kCaKFcMeEiX+wyhdq91QZ+eNfxVmypZ7koBX47FhS0EmdSUDODA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4eee1e861a6de58d8c4da8feb6d287665a0a2743d91bf74d1f7a8f74a83c2e7d","last_reissued_at":"2026-05-18T01:22:13.021783Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:22:13.021783Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1601.05764","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":"2026-05-18T01:22:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3W28DI7zHqhvSSbgpnkOT3gy3PnnyE2OPBr+ID7rDjr5/caXEhDUphPszXdZqXjb4mZw8YN7rVce8NvREBVOBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T12:44:26.762306Z"},"content_sha256":"b6966df10f00e7ec2b2b6ccb3015a0915eeb787f0b3b5fbf8b7a2ea6d235ac26","schema_version":"1.0","event_id":"sha256:b6966df10f00e7ec2b2b6ccb3015a0915eeb787f0b3b5fbf8b7a2ea6d235ac26"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:J3XB5BQ2NXSY3DCNVD7LNUUHMZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Confidence-Based Approach for Balancing Fairness and Accuracy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.LG","authors_text":"\\'Ad\\'am D. Lelkes, Benjamin Fish, Jeremy Kun","submitted_at":"2016-01-21T19:48:07Z","abstract_excerpt":"We study three classical machine learning algorithms in the context of algorithmic fairness: adaptive boosting, support vector machines, and logistic regression. Our goal is to maintain the high accuracy of these learning algorithms while reducing the degree to which they discriminate against individuals because of their membership in a protected group.\n  Our first contribution is a method for achieving fairness by shifting the decision boundary for the protected group. The method is based on the theory of margins for boosting. Our method performs comparably to or outperforms previous algorith"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1601.05764","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":"2026-05-18T01:22:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Xq1ryDY1AT6kMHPkMiVh7W9dWd4lzQq9XdR0ey9XTwZ0ir6mSk7dMExtK/n8mLxUcVpZkueuCrcNCMyQUBMtBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T12:44:26.762668Z"},"content_sha256":"4dcd1a7db79ab90cc8a2bad22e9eb6c97028715eaaae01cc2fe77200c41060b1","schema_version":"1.0","event_id":"sha256:4dcd1a7db79ab90cc8a2bad22e9eb6c97028715eaaae01cc2fe77200c41060b1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/J3XB5BQ2NXSY3DCNVD7LNUUHMZ/bundle.json","state_url":"https://pith.science/pith/J3XB5BQ2NXSY3DCNVD7LNUUHMZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/J3XB5BQ2NXSY3DCNVD7LNUUHMZ/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":"2026-06-02T12:44:26Z","links":{"resolver":"https://pith.science/pith/J3XB5BQ2NXSY3DCNVD7LNUUHMZ","bundle":"https://pith.science/pith/J3XB5BQ2NXSY3DCNVD7LNUUHMZ/bundle.json","state":"https://pith.science/pith/J3XB5BQ2NXSY3DCNVD7LNUUHMZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/J3XB5BQ2NXSY3DCNVD7LNUUHMZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:J3XB5BQ2NXSY3DCNVD7LNUUHMZ","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":"9ec6443b33df5bcd6e605892b73f1e39fb4d56fffe11de3dbc2d717c318811ce","cross_cats_sorted":["cs.CY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-01-21T19:48:07Z","title_canon_sha256":"f3cbc42466f6db2452721c83398c57317410911dd859cd3812479c403b615c8c"},"schema_version":"1.0","source":{"id":"1601.05764","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1601.05764","created_at":"2026-05-18T01:22:13Z"},{"alias_kind":"arxiv_version","alias_value":"1601.05764v1","created_at":"2026-05-18T01:22:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1601.05764","created_at":"2026-05-18T01:22:13Z"},{"alias_kind":"pith_short_12","alias_value":"J3XB5BQ2NXSY","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_16","alias_value":"J3XB5BQ2NXSY3DCN","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_8","alias_value":"J3XB5BQ2","created_at":"2026-05-18T12:30:22Z"}],"graph_snapshots":[{"event_id":"sha256:4dcd1a7db79ab90cc8a2bad22e9eb6c97028715eaaae01cc2fe77200c41060b1","target":"graph","created_at":"2026-05-18T01:22:13Z","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":"We study three classical machine learning algorithms in the context of algorithmic fairness: adaptive boosting, support vector machines, and logistic regression. Our goal is to maintain the high accuracy of these learning algorithms while reducing the degree to which they discriminate against individuals because of their membership in a protected group.\n  Our first contribution is a method for achieving fairness by shifting the decision boundary for the protected group. The method is based on the theory of margins for boosting. Our method performs comparably to or outperforms previous algorith","authors_text":"\\'Ad\\'am D. Lelkes, Benjamin Fish, Jeremy Kun","cross_cats":["cs.CY"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-01-21T19:48:07Z","title":"A Confidence-Based Approach for Balancing Fairness and Accuracy"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1601.05764","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:b6966df10f00e7ec2b2b6ccb3015a0915eeb787f0b3b5fbf8b7a2ea6d235ac26","target":"record","created_at":"2026-05-18T01:22:13Z","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":"9ec6443b33df5bcd6e605892b73f1e39fb4d56fffe11de3dbc2d717c318811ce","cross_cats_sorted":["cs.CY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-01-21T19:48:07Z","title_canon_sha256":"f3cbc42466f6db2452721c83398c57317410911dd859cd3812479c403b615c8c"},"schema_version":"1.0","source":{"id":"1601.05764","kind":"arxiv","version":1}},"canonical_sha256":"4eee1e861a6de58d8c4da8feb6d287665a0a2743d91bf74d1f7a8f74a83c2e7d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4eee1e861a6de58d8c4da8feb6d287665a0a2743d91bf74d1f7a8f74a83c2e7d","first_computed_at":"2026-05-18T01:22:13.021783Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:22:13.021783Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zrl/ekvMhbg2X6zm6PmtLFtX7WonEplgAA+kCaKFcMeEiX+wyhdq91QZ+eNfxVmypZ7koBX47FhS0EmdSUDODA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:22:13.022277Z","signed_message":"canonical_sha256_bytes"},"source_id":"1601.05764","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b6966df10f00e7ec2b2b6ccb3015a0915eeb787f0b3b5fbf8b7a2ea6d235ac26","sha256:4dcd1a7db79ab90cc8a2bad22e9eb6c97028715eaaae01cc2fe77200c41060b1"],"state_sha256":"6a2a419a23ae2cc351345607139e8cead34fd1d2fadcc93832bf519b72669284"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XXWs7MuaOdigVFv+N7eM1ZABtBBEhdUbcVTPrb6QUIf5vPSpKfEs9Lwf4R+f7kz2F6vxeGHmpITECoDJfgO8AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T12:44:26.764713Z","bundle_sha256":"38ce7b67d344a68d15f5359c0c4e4baa82677017cd017d4693f5d97d9b069b70"}}