{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:K3C4VH5ZSQVZMYLC7CQP6FTZWS","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":"1e938ef1f3399b33be9cbe54f7d2e7c0e90d007cde2680200abd6b7f1c865bf4","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2020-06-17T05:22:33Z","title_canon_sha256":"442876f6c88a247da13f3e844e6ab167685f4add0a934bca9a35b0914a0fead8"},"schema_version":"1.0","source":{"id":"2006.09663","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.09663","created_at":"2026-07-05T01:11:04Z"},{"alias_kind":"arxiv_version","alias_value":"2006.09663v1","created_at":"2026-07-05T01:11:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.09663","created_at":"2026-07-05T01:11:04Z"},{"alias_kind":"pith_short_12","alias_value":"K3C4VH5ZSQVZ","created_at":"2026-07-05T01:11:04Z"},{"alias_kind":"pith_short_16","alias_value":"K3C4VH5ZSQVZMYLC","created_at":"2026-07-05T01:11:04Z"},{"alias_kind":"pith_short_8","alias_value":"K3C4VH5Z","created_at":"2026-07-05T01:11:04Z"}],"graph_snapshots":[{"event_id":"sha256:0d4a67301888bba56d0b61b520b96a518cbc884347a856a8ddf7748940895a79","target":"graph","created_at":"2026-07-05T01:11:04Z","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/2006.09663/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Machine learning (ML) fairness research tends to focus primarily on mathematically-based interventions on often opaque algorithms or models and/or their immediate inputs and outputs. Such oversimplified mathematical models abstract away the underlying societal context where ML models are conceived, developed, and ultimately deployed. As fairness itself is a socially constructed concept that originates from that societal context along with the model inputs and the models themselves, a lack of an in-depth understanding of societal context can easily undermine the pursuit of ML fairness. In this ","authors_text":"Andrew Smart, Donald Martin Jr., Jill Kuhlberg, Vinodkumar Prabhakaran, William S. Isaac","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2020-06-17T05:22:33Z","title":"Extending the Machine Learning Abstraction Boundary: A Complex Systems Approach to Incorporate Societal Context"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.09663","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:f346aa564b768877c73d306517ccbd4720d39a98d29ffd1dbfc1cf7bad8a5d88","target":"record","created_at":"2026-07-05T01:11:04Z","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":"1e938ef1f3399b33be9cbe54f7d2e7c0e90d007cde2680200abd6b7f1c865bf4","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2020-06-17T05:22:33Z","title_canon_sha256":"442876f6c88a247da13f3e844e6ab167685f4add0a934bca9a35b0914a0fead8"},"schema_version":"1.0","source":{"id":"2006.09663","kind":"arxiv","version":1}},"canonical_sha256":"56c5ca9fb9942b966162f8a0ff1679b4b77dcb143383fd759874d27faa1d21c1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"56c5ca9fb9942b966162f8a0ff1679b4b77dcb143383fd759874d27faa1d21c1","first_computed_at":"2026-07-05T01:11:04.015576Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:11:04.015576Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"sAp3704Yd2fyYV6WvF04Vsw+bywXqBRog2GlJtoCqdU/T0Raq6RmJz1+OwCf1bRLS5ncs6Mu7P/LDN9PjlQPBg==","signature_status":"signed_v1","signed_at":"2026-07-05T01:11:04.016007Z","signed_message":"canonical_sha256_bytes"},"source_id":"2006.09663","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f346aa564b768877c73d306517ccbd4720d39a98d29ffd1dbfc1cf7bad8a5d88","sha256:0d4a67301888bba56d0b61b520b96a518cbc884347a856a8ddf7748940895a79"],"state_sha256":"6a30870a54c049fc3046f873bfd31c32f6c9152e618ae6a090f9bc0925cce89e"}