{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:5BKAUUNJ7GME2QGFO7JAZANV7I","short_pith_number":"pith:5BKAUUNJ","canonical_record":{"source":{"id":"2205.08875","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-18T11:59:22Z","cross_cats_sorted":["cs.CY"],"title_canon_sha256":"3fa0be3a334a61c53bf4294977e6d578d3d6fd74a56c75d8ebdc166e6fc4e806","abstract_canon_sha256":"d05576f1ea6323698e57fd7133a6f3d17bd40e4505e693588fd4a604e804b92a"},"schema_version":"1.0"},"canonical_sha256":"e8540a51a9f9984d40c577d20c81b5fa1ae11f1adc08a2d79b01c8c565d9001f","source":{"kind":"arxiv","id":"2205.08875","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.08875","created_at":"2026-07-05T04:24:30Z"},{"alias_kind":"arxiv_version","alias_value":"2205.08875v1","created_at":"2026-07-05T04:24:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.08875","created_at":"2026-07-05T04:24:30Z"},{"alias_kind":"pith_short_12","alias_value":"5BKAUUNJ7GME","created_at":"2026-07-05T04:24:30Z"},{"alias_kind":"pith_short_16","alias_value":"5BKAUUNJ7GME2QGF","created_at":"2026-07-05T04:24:30Z"},{"alias_kind":"pith_short_8","alias_value":"5BKAUUNJ","created_at":"2026-07-05T04:24:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:5BKAUUNJ7GME2QGFO7JAZANV7I","target":"record","payload":{"canonical_record":{"source":{"id":"2205.08875","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-18T11:59:22Z","cross_cats_sorted":["cs.CY"],"title_canon_sha256":"3fa0be3a334a61c53bf4294977e6d578d3d6fd74a56c75d8ebdc166e6fc4e806","abstract_canon_sha256":"d05576f1ea6323698e57fd7133a6f3d17bd40e4505e693588fd4a604e804b92a"},"schema_version":"1.0"},"canonical_sha256":"e8540a51a9f9984d40c577d20c81b5fa1ae11f1adc08a2d79b01c8c565d9001f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:24:30.582306Z","signature_b64":"IzBiIr1KfwM/kvPM+hnFVRz0UNsCDDVfAfka9bvhy8y3fOSeUh+BerlDmvPHjR4lVNEY+BbBUgIrP/hFG2v8AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e8540a51a9f9984d40c577d20c81b5fa1ae11f1adc08a2d79b01c8c565d9001f","last_reissued_at":"2026-07-05T04:24:30.581878Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:24:30.581878Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2205.08875","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":"2026-07-05T04:24:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gn4dnik8lo1oib9chK4cq1cw1ZQg8amE+c2t1BRN1WkB8fgIR3XXhJI4pTcq70i9zgivIOyWzkPmZjuHzvvCBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T12:56:35.609877Z"},"content_sha256":"190790afaf9370ce7d67270c40510e6788f0b9d67cc8aa8cf4b171a27bb3e887","schema_version":"1.0","event_id":"sha256:190790afaf9370ce7d67270c40510e6788f0b9d67cc8aa8cf4b171a27bb3e887"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:5BKAUUNJ7GME2QGFO7JAZANV7I","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi-disciplinary fairness considerations in machine learning for clinical trials","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.LG","authors_text":"Adrian Weller, Isabel Chien, Niki Kilbertus, Nina Deliu, Richard E. Turner, Sofia S. Villar","submitted_at":"2022-05-18T11:59:22Z","abstract_excerpt":"While interest in the application of machine learning to improve healthcare has grown tremendously in recent years, a number of barriers prevent deployment in medical practice. A notable concern is the potential to exacerbate entrenched biases and existing health disparities in society. The area of fairness in machine learning seeks to address these issues of equity; however, appropriate approaches are context-dependent, necessitating domain-specific consideration. We focus on clinical trials, i.e., research studies conducted on humans to evaluate medical treatments. Clinical trials are a rela"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.08875","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/2205.08875/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":"2026-07-05T04:24:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Oi7OiaX/EJCIDbJYvHy3eEnijJyz2Dg8ex/tV+LEUqbwor0Urx1WWi9CpWBw9AkDgaA7a4kVHY6SeUyIFrgcDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T12:56:35.610376Z"},"content_sha256":"666613206451b3a7a8d55d9f3f79ef17ee668ad275ecf9439c0ea175bd33f259","schema_version":"1.0","event_id":"sha256:666613206451b3a7a8d55d9f3f79ef17ee668ad275ecf9439c0ea175bd33f259"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5BKAUUNJ7GME2QGFO7JAZANV7I/bundle.json","state_url":"https://pith.science/pith/5BKAUUNJ7GME2QGFO7JAZANV7I/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5BKAUUNJ7GME2QGFO7JAZANV7I/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":"2026-08-08T12:56:35Z","links":{"resolver":"https://pith.science/pith/5BKAUUNJ7GME2QGFO7JAZANV7I","bundle":"https://pith.science/pith/5BKAUUNJ7GME2QGFO7JAZANV7I/bundle.json","state":"https://pith.science/pith/5BKAUUNJ7GME2QGFO7JAZANV7I/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5BKAUUNJ7GME2QGFO7JAZANV7I/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:5BKAUUNJ7GME2QGFO7JAZANV7I","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":"d05576f1ea6323698e57fd7133a6f3d17bd40e4505e693588fd4a604e804b92a","cross_cats_sorted":["cs.CY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-18T11:59:22Z","title_canon_sha256":"3fa0be3a334a61c53bf4294977e6d578d3d6fd74a56c75d8ebdc166e6fc4e806"},"schema_version":"1.0","source":{"id":"2205.08875","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.08875","created_at":"2026-07-05T04:24:30Z"},{"alias_kind":"arxiv_version","alias_value":"2205.08875v1","created_at":"2026-07-05T04:24:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.08875","created_at":"2026-07-05T04:24:30Z"},{"alias_kind":"pith_short_12","alias_value":"5BKAUUNJ7GME","created_at":"2026-07-05T04:24:30Z"},{"alias_kind":"pith_short_16","alias_value":"5BKAUUNJ7GME2QGF","created_at":"2026-07-05T04:24:30Z"},{"alias_kind":"pith_short_8","alias_value":"5BKAUUNJ","created_at":"2026-07-05T04:24:30Z"}],"graph_snapshots":[{"event_id":"sha256:666613206451b3a7a8d55d9f3f79ef17ee668ad275ecf9439c0ea175bd33f259","target":"graph","created_at":"2026-07-05T04:24:30Z","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/2205.08875/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While interest in the application of machine learning to improve healthcare has grown tremendously in recent years, a number of barriers prevent deployment in medical practice. A notable concern is the potential to exacerbate entrenched biases and existing health disparities in society. The area of fairness in machine learning seeks to address these issues of equity; however, appropriate approaches are context-dependent, necessitating domain-specific consideration. We focus on clinical trials, i.e., research studies conducted on humans to evaluate medical treatments. Clinical trials are a rela","authors_text":"Adrian Weller, Isabel Chien, Niki Kilbertus, Nina Deliu, Richard E. Turner, Sofia S. Villar","cross_cats":["cs.CY"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-18T11:59:22Z","title":"Multi-disciplinary fairness considerations in machine learning for clinical trials"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.08875","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":"sha256:190790afaf9370ce7d67270c40510e6788f0b9d67cc8aa8cf4b171a27bb3e887","target":"record","created_at":"2026-07-05T04:24:30Z","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":"d05576f1ea6323698e57fd7133a6f3d17bd40e4505e693588fd4a604e804b92a","cross_cats_sorted":["cs.CY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-18T11:59:22Z","title_canon_sha256":"3fa0be3a334a61c53bf4294977e6d578d3d6fd74a56c75d8ebdc166e6fc4e806"},"schema_version":"1.0","source":{"id":"2205.08875","kind":"arxiv","version":1}},"canonical_sha256":"e8540a51a9f9984d40c577d20c81b5fa1ae11f1adc08a2d79b01c8c565d9001f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e8540a51a9f9984d40c577d20c81b5fa1ae11f1adc08a2d79b01c8c565d9001f","first_computed_at":"2026-07-05T04:24:30.581878Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:24:30.581878Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"IzBiIr1KfwM/kvPM+hnFVRz0UNsCDDVfAfka9bvhy8y3fOSeUh+BerlDmvPHjR4lVNEY+BbBUgIrP/hFG2v8AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:24:30.582306Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.08875","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:190790afaf9370ce7d67270c40510e6788f0b9d67cc8aa8cf4b171a27bb3e887","sha256:666613206451b3a7a8d55d9f3f79ef17ee668ad275ecf9439c0ea175bd33f259"],"state_sha256":"1b25ea66c03c2431f50825028cc39c60d4197236a4c160b80c7fe7c9e7d3fd0a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4ScDJVk1oCKEQWpvOX9foa1LUD4+b4IIx6M9N9Wk8B5G/CQVr7/z0htNH8FXmS42q4mvcbn6L6iBuL8T70IXBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T12:56:35.615713Z","bundle_sha256":"9541909dfaae72a474710544e7152cde078051cbbca4314cef786136e4a1caf4"}}