{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:TQD4O5G64FLTDW5WIX3J7J6OSQ","short_pith_number":"pith:TQD4O5G6","canonical_record":{"source":{"id":"1909.01866","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-02T20:36:19Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"1352978dfe3a0784f3c1b2a105018b241365a8a503452fb1e0273ccdfd987e89","abstract_canon_sha256":"617ab7a1eed95691b9d234a2503a0311aced2e7bf7a4e967d764b9543abfed7e"},"schema_version":"1.0"},"canonical_sha256":"9c07c774dee15731dbb645f69fa7ce9425c546632df6ac359315176393f8790f","source":{"kind":"arxiv","id":"1909.01866","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.01866","created_at":"2026-07-05T00:02:21Z"},{"alias_kind":"arxiv_version","alias_value":"1909.01866v1","created_at":"2026-07-05T00:02:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.01866","created_at":"2026-07-05T00:02:21Z"},{"alias_kind":"pith_short_12","alias_value":"TQD4O5G64FLT","created_at":"2026-07-05T00:02:21Z"},{"alias_kind":"pith_short_16","alias_value":"TQD4O5G64FLTDW5W","created_at":"2026-07-05T00:02:21Z"},{"alias_kind":"pith_short_8","alias_value":"TQD4O5G6","created_at":"2026-07-05T00:02:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:TQD4O5G64FLTDW5WIX3J7J6OSQ","target":"record","payload":{"canonical_record":{"source":{"id":"1909.01866","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-02T20:36:19Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"1352978dfe3a0784f3c1b2a105018b241365a8a503452fb1e0273ccdfd987e89","abstract_canon_sha256":"617ab7a1eed95691b9d234a2503a0311aced2e7bf7a4e967d764b9543abfed7e"},"schema_version":"1.0"},"canonical_sha256":"9c07c774dee15731dbb645f69fa7ce9425c546632df6ac359315176393f8790f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:02:21.194564Z","signature_b64":"fWPQgdS7Lf5iDF1FQCIAIKqiDs1dtlBtSml7lnjY3RkdtZIqU/RpV95ZuotR29kOPbaj1EVKpu0gZ7WVfpb9BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9c07c774dee15731dbb645f69fa7ce9425c546632df6ac359315176393f8790f","last_reissued_at":"2026-07-05T00:02:21.194204Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:02:21.194204Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1909.01866","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":"2026-07-05T00:02:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"u9m+nHQ7S17etbKbXIChpi2maP4eWT/IKM/6tkti/iSzY3xG5ddMBOYwsADx6G0oiMApA/oTkHIDP4FGTl8BCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T15:57:37.465789Z"},"content_sha256":"d96df2ee1c725a70d7495b4c48026dda3eb8f9bfdeacea68703ce5de6da5a64c","schema_version":"1.0","event_id":"sha256:d96df2ee1c725a70d7495b4c48026dda3eb8f9bfdeacea68703ce5de6da5a64c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:TQD4O5G64FLTDW5WIX3J7J6OSQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Understanding Bias in Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Daniela Oelke, Jindong Gu","submitted_at":"2019-09-02T20:36:19Z","abstract_excerpt":"Bias is known to be an impediment to fair decisions in many domains such as human resources, the public sector, health care etc. Recently, hope has been expressed that the use of machine learning methods for taking such decisions would diminish or even resolve the problem. At the same time, machine learning experts warn that machine learning models can be biased as well. In this article, our goal is to explain the issue of bias in machine learning from a technical perspective and to illustrate the impact that biased data can have on a machine learning model. To reach such a goal, we develop in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.01866","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/1909.01866/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":"2026-07-05T00:02:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iZPA9VQTJ7dpN23frLlIn5ATMzS6U5DrIAfqROE6E4HbgngmIT1PguHZ0ixMFMLyT/OSraIz8ZKqsbLem+U5DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T15:57:37.466336Z"},"content_sha256":"c5d3b140df05dc2477011b11e5c397a7d275962f96f4abf1cf6d08e74b28ca0f","schema_version":"1.0","event_id":"sha256:c5d3b140df05dc2477011b11e5c397a7d275962f96f4abf1cf6d08e74b28ca0f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TQD4O5G64FLTDW5WIX3J7J6OSQ/bundle.json","state_url":"https://pith.science/pith/TQD4O5G64FLTDW5WIX3J7J6OSQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TQD4O5G64FLTDW5WIX3J7J6OSQ/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":"2026-08-06T15:57:37Z","links":{"resolver":"https://pith.science/pith/TQD4O5G64FLTDW5WIX3J7J6OSQ","bundle":"https://pith.science/pith/TQD4O5G64FLTDW5WIX3J7J6OSQ/bundle.json","state":"https://pith.science/pith/TQD4O5G64FLTDW5WIX3J7J6OSQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TQD4O5G64FLTDW5WIX3J7J6OSQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:TQD4O5G64FLTDW5WIX3J7J6OSQ","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":"617ab7a1eed95691b9d234a2503a0311aced2e7bf7a4e967d764b9543abfed7e","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-02T20:36:19Z","title_canon_sha256":"1352978dfe3a0784f3c1b2a105018b241365a8a503452fb1e0273ccdfd987e89"},"schema_version":"1.0","source":{"id":"1909.01866","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.01866","created_at":"2026-07-05T00:02:21Z"},{"alias_kind":"arxiv_version","alias_value":"1909.01866v1","created_at":"2026-07-05T00:02:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.01866","created_at":"2026-07-05T00:02:21Z"},{"alias_kind":"pith_short_12","alias_value":"TQD4O5G64FLT","created_at":"2026-07-05T00:02:21Z"},{"alias_kind":"pith_short_16","alias_value":"TQD4O5G64FLTDW5W","created_at":"2026-07-05T00:02:21Z"},{"alias_kind":"pith_short_8","alias_value":"TQD4O5G6","created_at":"2026-07-05T00:02:21Z"}],"graph_snapshots":[{"event_id":"sha256:c5d3b140df05dc2477011b11e5c397a7d275962f96f4abf1cf6d08e74b28ca0f","target":"graph","created_at":"2026-07-05T00:02:21Z","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/1909.01866/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Bias is known to be an impediment to fair decisions in many domains such as human resources, the public sector, health care etc. Recently, hope has been expressed that the use of machine learning methods for taking such decisions would diminish or even resolve the problem. At the same time, machine learning experts warn that machine learning models can be biased as well. In this article, our goal is to explain the issue of bias in machine learning from a technical perspective and to illustrate the impact that biased data can have on a machine learning model. To reach such a goal, we develop in","authors_text":"Daniela Oelke, Jindong Gu","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-02T20:36:19Z","title":"Understanding Bias in Machine Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.01866","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:d96df2ee1c725a70d7495b4c48026dda3eb8f9bfdeacea68703ce5de6da5a64c","target":"record","created_at":"2026-07-05T00:02:21Z","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":"617ab7a1eed95691b9d234a2503a0311aced2e7bf7a4e967d764b9543abfed7e","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-02T20:36:19Z","title_canon_sha256":"1352978dfe3a0784f3c1b2a105018b241365a8a503452fb1e0273ccdfd987e89"},"schema_version":"1.0","source":{"id":"1909.01866","kind":"arxiv","version":1}},"canonical_sha256":"9c07c774dee15731dbb645f69fa7ce9425c546632df6ac359315176393f8790f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9c07c774dee15731dbb645f69fa7ce9425c546632df6ac359315176393f8790f","first_computed_at":"2026-07-05T00:02:21.194204Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:02:21.194204Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fWPQgdS7Lf5iDF1FQCIAIKqiDs1dtlBtSml7lnjY3RkdtZIqU/RpV95ZuotR29kOPbaj1EVKpu0gZ7WVfpb9BA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:02:21.194564Z","signed_message":"canonical_sha256_bytes"},"source_id":"1909.01866","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d96df2ee1c725a70d7495b4c48026dda3eb8f9bfdeacea68703ce5de6da5a64c","sha256:c5d3b140df05dc2477011b11e5c397a7d275962f96f4abf1cf6d08e74b28ca0f"],"state_sha256":"15658289967cfdded9005ed497d822e640474a0be06bfc84c3a247bd49aa9862"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"K3Up853wXMloznlr1t34qLUgDBmP1q76O+oidIwzZNphTWJQaOrzQL1bM9hhPNP+nApVJx4EgxHouj76acjuCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T15:57:37.470558Z","bundle_sha256":"d3716be7f4b5a91a935234acc5302530d7c61d6900e038ce9005af8597aa4546"}}