{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:4ARW5DTVKSETEILZBE7E3C2ORJ","short_pith_number":"pith:4ARW5DTV","canonical_record":{"source":{"id":"1904.05419","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-10T20:07:35Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"3554ef74db07c0ca30bfb2fd736250b2645840248eb2932cf7d6106746f64211","abstract_canon_sha256":"99e858db4d5e7be6dda1177db02e6aa3fe8caed61d9afe00b53169d0930e1bc5"},"schema_version":"1.0"},"canonical_sha256":"e0236e8e755489322179093e4d8b4e8a7e8c7deb7dee71764d1ec839fa0c3802","source":{"kind":"arxiv","id":"1904.05419","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.05419","created_at":"2026-07-05T03:26:11Z"},{"alias_kind":"arxiv_version","alias_value":"1904.05419v4","created_at":"2026-07-05T03:26:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.05419","created_at":"2026-07-05T03:26:11Z"},{"alias_kind":"pith_short_12","alias_value":"4ARW5DTVKSET","created_at":"2026-07-05T03:26:11Z"},{"alias_kind":"pith_short_16","alias_value":"4ARW5DTVKSETEILZ","created_at":"2026-07-05T03:26:11Z"},{"alias_kind":"pith_short_8","alias_value":"4ARW5DTV","created_at":"2026-07-05T03:26:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:4ARW5DTVKSETEILZBE7E3C2ORJ","target":"record","payload":{"canonical_record":{"source":{"id":"1904.05419","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-10T20:07:35Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"3554ef74db07c0ca30bfb2fd736250b2645840248eb2932cf7d6106746f64211","abstract_canon_sha256":"99e858db4d5e7be6dda1177db02e6aa3fe8caed61d9afe00b53169d0930e1bc5"},"schema_version":"1.0"},"canonical_sha256":"e0236e8e755489322179093e4d8b4e8a7e8c7deb7dee71764d1ec839fa0c3802","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:26:11.785999Z","signature_b64":"tvgtR1y1qla7nuLaZsfKZcI3/dZE2fnXAi16V1EBS4tnZzUmurtMPLAXjR3Ai3eJu2Tb7eSp1EAfkOv0nEwxAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e0236e8e755489322179093e4d8b4e8a7e8c7deb7dee71764d1ec839fa0c3802","last_reissued_at":"2026-07-05T03:26:11.785565Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:26:11.785565Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1904.05419","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:26:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bnuxBEJji2NBaRcB7P55wBaJOrSLJKGi0I7fnvAMF6CL3ko/u8O1BlQBRZpenQyftE7prUDPOu+hY8NKQPDBDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T10:03:22.406514Z"},"content_sha256":"74fa338db700bc080df0965029ef738d8b85699a0aeec491b216003d7c2c9a30","schema_version":"1.0","event_id":"sha256:74fa338db700bc080df0965029ef738d8b85699a0aeec491b216003d7c2c9a30"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:4ARW5DTVKSETEILZBE7E3C2ORJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"\\'Angel Alexander Cabrera, Duen Horng Chau, Fred Hohman, Jamie Morgenstern, Minsuk Kahng, Will Epperson","submitted_at":"2019-04-10T20:07:35Z","abstract_excerpt":"The growing capability and accessibility of machine learning has led to its application to many real-world domains and data about people. Despite the benefits algorithmic systems may bring, models can reflect, inject, or exacerbate implicit and explicit societal biases into their outputs, disadvantaging certain demographic subgroups. Discovering which biases a machine learning model has introduced is a great challenge, due to the numerous definitions of fairness and the large number of potentially impacted subgroups. We present FairVis, a mixed-initiative visual analytics system that integrate"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.05419","kind":"arxiv","version":4},"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/1904.05419/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":"2026-07-05T03:26:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+jH7Zwck8vCXn6YF+AsvHMsuO6GguCzJ2IE6w1YYbdPYCdlfYSlunfEO4Yok+yNPYlcSH1FZfeqMsA62PwltAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T10:03:22.407012Z"},"content_sha256":"3780ce93f20e41f2094d198fedf450fe6cc817ae81f17b0a5627693cf134b3b6","schema_version":"1.0","event_id":"sha256:3780ce93f20e41f2094d198fedf450fe6cc817ae81f17b0a5627693cf134b3b6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4ARW5DTVKSETEILZBE7E3C2ORJ/bundle.json","state_url":"https://pith.science/pith/4ARW5DTVKSETEILZBE7E3C2ORJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4ARW5DTVKSETEILZBE7E3C2ORJ/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":"2026-08-01T10:03:22Z","links":{"resolver":"https://pith.science/pith/4ARW5DTVKSETEILZBE7E3C2ORJ","bundle":"https://pith.science/pith/4ARW5DTVKSETEILZBE7E3C2ORJ/bundle.json","state":"https://pith.science/pith/4ARW5DTVKSETEILZBE7E3C2ORJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4ARW5DTVKSETEILZBE7E3C2ORJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:4ARW5DTVKSETEILZBE7E3C2ORJ","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":"99e858db4d5e7be6dda1177db02e6aa3fe8caed61d9afe00b53169d0930e1bc5","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-10T20:07:35Z","title_canon_sha256":"3554ef74db07c0ca30bfb2fd736250b2645840248eb2932cf7d6106746f64211"},"schema_version":"1.0","source":{"id":"1904.05419","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.05419","created_at":"2026-07-05T03:26:11Z"},{"alias_kind":"arxiv_version","alias_value":"1904.05419v4","created_at":"2026-07-05T03:26:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.05419","created_at":"2026-07-05T03:26:11Z"},{"alias_kind":"pith_short_12","alias_value":"4ARW5DTVKSET","created_at":"2026-07-05T03:26:11Z"},{"alias_kind":"pith_short_16","alias_value":"4ARW5DTVKSETEILZ","created_at":"2026-07-05T03:26:11Z"},{"alias_kind":"pith_short_8","alias_value":"4ARW5DTV","created_at":"2026-07-05T03:26:11Z"}],"graph_snapshots":[{"event_id":"sha256:3780ce93f20e41f2094d198fedf450fe6cc817ae81f17b0a5627693cf134b3b6","target":"graph","created_at":"2026-07-05T03:26:11Z","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/1904.05419/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The growing capability and accessibility of machine learning has led to its application to many real-world domains and data about people. Despite the benefits algorithmic systems may bring, models can reflect, inject, or exacerbate implicit and explicit societal biases into their outputs, disadvantaging certain demographic subgroups. Discovering which biases a machine learning model has introduced is a great challenge, due to the numerous definitions of fairness and the large number of potentially impacted subgroups. We present FairVis, a mixed-initiative visual analytics system that integrate","authors_text":"\\'Angel Alexander Cabrera, Duen Horng Chau, Fred Hohman, Jamie Morgenstern, Minsuk Kahng, Will Epperson","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-10T20:07:35Z","title":"FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.05419","kind":"arxiv","version":4},"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":"sha256:74fa338db700bc080df0965029ef738d8b85699a0aeec491b216003d7c2c9a30","target":"record","created_at":"2026-07-05T03:26:11Z","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":"99e858db4d5e7be6dda1177db02e6aa3fe8caed61d9afe00b53169d0930e1bc5","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-10T20:07:35Z","title_canon_sha256":"3554ef74db07c0ca30bfb2fd736250b2645840248eb2932cf7d6106746f64211"},"schema_version":"1.0","source":{"id":"1904.05419","kind":"arxiv","version":4}},"canonical_sha256":"e0236e8e755489322179093e4d8b4e8a7e8c7deb7dee71764d1ec839fa0c3802","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e0236e8e755489322179093e4d8b4e8a7e8c7deb7dee71764d1ec839fa0c3802","first_computed_at":"2026-07-05T03:26:11.785565Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:26:11.785565Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tvgtR1y1qla7nuLaZsfKZcI3/dZE2fnXAi16V1EBS4tnZzUmurtMPLAXjR3Ai3eJu2Tb7eSp1EAfkOv0nEwxAw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:26:11.785999Z","signed_message":"canonical_sha256_bytes"},"source_id":"1904.05419","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:74fa338db700bc080df0965029ef738d8b85699a0aeec491b216003d7c2c9a30","sha256:3780ce93f20e41f2094d198fedf450fe6cc817ae81f17b0a5627693cf134b3b6"],"state_sha256":"39e15b5a7117eb3eac31470dc333c7c73affe1c6e37cc6b22cd8b77bde6508a5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JxfKXdky2kVgtQam4H18IdArKSmaPvVw77C3zASUQYniTZFJDQiW/lXe87F3392coKYb7eX0B4GnNTAnYaKKDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-01T10:03:22.410748Z","bundle_sha256":"a1b8b1de27797be23bc03228a758b0566ac9159c74521d947c1da9972fbf65ad"}}