{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:QH7PJ72OPGU36KNDGYOVLIG3FZ","short_pith_number":"pith:QH7PJ72O","canonical_record":{"source":{"id":"2102.13004","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-02-25T17:08:39Z","cross_cats_sorted":["cs.HC","stat.ML"],"title_canon_sha256":"ca760463de5ca0892dbf707a71dc5b51b6f26b7be4cef4c00c95c142ca4d1adc","abstract_canon_sha256":"2532efb87dc685d01bffd6efec8a2daa3237324b78a6d5a5df2b5b9494250cda"},"schema_version":"1.0"},"canonical_sha256":"81fef4ff4e79a9bf29a3361d55a0db2e55a4dc761a18e6406225fdd08e84261d","source":{"kind":"arxiv","id":"2102.13004","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2102.13004","created_at":"2026-07-05T03:39:57Z"},{"alias_kind":"arxiv_version","alias_value":"2102.13004v2","created_at":"2026-07-05T03:39:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.13004","created_at":"2026-07-05T03:39:57Z"},{"alias_kind":"pith_short_12","alias_value":"QH7PJ72OPGU3","created_at":"2026-07-05T03:39:57Z"},{"alias_kind":"pith_short_16","alias_value":"QH7PJ72OPGU36KND","created_at":"2026-07-05T03:39:57Z"},{"alias_kind":"pith_short_8","alias_value":"QH7PJ72O","created_at":"2026-07-05T03:39:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:QH7PJ72OPGU36KNDGYOVLIG3FZ","target":"record","payload":{"canonical_record":{"source":{"id":"2102.13004","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-02-25T17:08:39Z","cross_cats_sorted":["cs.HC","stat.ML"],"title_canon_sha256":"ca760463de5ca0892dbf707a71dc5b51b6f26b7be4cef4c00c95c142ca4d1adc","abstract_canon_sha256":"2532efb87dc685d01bffd6efec8a2daa3237324b78a6d5a5df2b5b9494250cda"},"schema_version":"1.0"},"canonical_sha256":"81fef4ff4e79a9bf29a3361d55a0db2e55a4dc761a18e6406225fdd08e84261d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:39:57.272807Z","signature_b64":"Fkmh0q4mE/oQz9VbsmuOf3V5CkXnbvrToRrb+tgmqM6s3JkymtXybZwA+1NgX2AMFTyBFudkxKaCtWaE5J5MBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"81fef4ff4e79a9bf29a3361d55a0db2e55a4dc761a18e6406225fdd08e84261d","last_reissued_at":"2026-07-05T03:39:57.272401Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:39:57.272401Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2102.13004","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:39:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q11+RY9CDElgeEkAyLOgshwjbF2fZlZtIxKbgXvRYedIaiuP6P8TjbhXZcDozKHJIvxMv8w6Ay83qQkXAfiWDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T18:05:41.693307Z"},"content_sha256":"81e620e8f0ff89ba458b51e360ca5bd8bd4f75b805b7284de53386197a7e446f","schema_version":"1.0","event_id":"sha256:81e620e8f0ff89ba458b51e360ca5bd8bd4f75b805b7284de53386197a7e446f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:QH7PJ72OPGU36KNDGYOVLIG3FZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Towards Unbiased and Accurate Deferral to Multiple Experts","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Krishnaram Kenthapadi, Matthew Lease, Vijay Keswani","submitted_at":"2021-02-25T17:08:39Z","abstract_excerpt":"Machine learning models are often implemented in cohort with humans in the pipeline, with the model having an option to defer to a domain expert in cases where it has low confidence in its inference. Our goal is to design mechanisms for ensuring accuracy and fairness in such prediction systems that combine machine learning model inferences and domain expert predictions. Prior work on \"deferral systems\" in classification settings has focused on the setting of a pipeline with a single expert and aimed to accommodate the inaccuracies and biases of this expert to simultaneously learn an inference "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.13004","kind":"arxiv","version":2},"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/2102.13004/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":"2026-07-05T03:39:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"S0DJmGrfdIqP2+gDkgXxGrQyj1o4rPoZnosJBjJEINsgMzXL9Io/IUjXEC3B06rhkhWFFOySmq1LyQ8yVfu/Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T18:05:41.693828Z"},"content_sha256":"af3c889b8c19c670e9844022deb3cee4417ebf4439675bd0bf483c14a78cc451","schema_version":"1.0","event_id":"sha256:af3c889b8c19c670e9844022deb3cee4417ebf4439675bd0bf483c14a78cc451"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QH7PJ72OPGU36KNDGYOVLIG3FZ/bundle.json","state_url":"https://pith.science/pith/QH7PJ72OPGU36KNDGYOVLIG3FZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QH7PJ72OPGU36KNDGYOVLIG3FZ/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":"2026-08-07T18:05:41Z","links":{"resolver":"https://pith.science/pith/QH7PJ72OPGU36KNDGYOVLIG3FZ","bundle":"https://pith.science/pith/QH7PJ72OPGU36KNDGYOVLIG3FZ/bundle.json","state":"https://pith.science/pith/QH7PJ72OPGU36KNDGYOVLIG3FZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QH7PJ72OPGU36KNDGYOVLIG3FZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:QH7PJ72OPGU36KNDGYOVLIG3FZ","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":"2532efb87dc685d01bffd6efec8a2daa3237324b78a6d5a5df2b5b9494250cda","cross_cats_sorted":["cs.HC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-02-25T17:08:39Z","title_canon_sha256":"ca760463de5ca0892dbf707a71dc5b51b6f26b7be4cef4c00c95c142ca4d1adc"},"schema_version":"1.0","source":{"id":"2102.13004","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2102.13004","created_at":"2026-07-05T03:39:57Z"},{"alias_kind":"arxiv_version","alias_value":"2102.13004v2","created_at":"2026-07-05T03:39:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.13004","created_at":"2026-07-05T03:39:57Z"},{"alias_kind":"pith_short_12","alias_value":"QH7PJ72OPGU3","created_at":"2026-07-05T03:39:57Z"},{"alias_kind":"pith_short_16","alias_value":"QH7PJ72OPGU36KND","created_at":"2026-07-05T03:39:57Z"},{"alias_kind":"pith_short_8","alias_value":"QH7PJ72O","created_at":"2026-07-05T03:39:57Z"}],"graph_snapshots":[{"event_id":"sha256:af3c889b8c19c670e9844022deb3cee4417ebf4439675bd0bf483c14a78cc451","target":"graph","created_at":"2026-07-05T03:39:57Z","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/2102.13004/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Machine learning models are often implemented in cohort with humans in the pipeline, with the model having an option to defer to a domain expert in cases where it has low confidence in its inference. Our goal is to design mechanisms for ensuring accuracy and fairness in such prediction systems that combine machine learning model inferences and domain expert predictions. Prior work on \"deferral systems\" in classification settings has focused on the setting of a pipeline with a single expert and aimed to accommodate the inaccuracies and biases of this expert to simultaneously learn an inference ","authors_text":"Krishnaram Kenthapadi, Matthew Lease, Vijay Keswani","cross_cats":["cs.HC","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-02-25T17:08:39Z","title":"Towards Unbiased and Accurate Deferral to Multiple Experts"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.13004","kind":"arxiv","version":2},"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":"sha256:81e620e8f0ff89ba458b51e360ca5bd8bd4f75b805b7284de53386197a7e446f","target":"record","created_at":"2026-07-05T03:39:57Z","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":"2532efb87dc685d01bffd6efec8a2daa3237324b78a6d5a5df2b5b9494250cda","cross_cats_sorted":["cs.HC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-02-25T17:08:39Z","title_canon_sha256":"ca760463de5ca0892dbf707a71dc5b51b6f26b7be4cef4c00c95c142ca4d1adc"},"schema_version":"1.0","source":{"id":"2102.13004","kind":"arxiv","version":2}},"canonical_sha256":"81fef4ff4e79a9bf29a3361d55a0db2e55a4dc761a18e6406225fdd08e84261d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"81fef4ff4e79a9bf29a3361d55a0db2e55a4dc761a18e6406225fdd08e84261d","first_computed_at":"2026-07-05T03:39:57.272401Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:39:57.272401Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Fkmh0q4mE/oQz9VbsmuOf3V5CkXnbvrToRrb+tgmqM6s3JkymtXybZwA+1NgX2AMFTyBFudkxKaCtWaE5J5MBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T03:39:57.272807Z","signed_message":"canonical_sha256_bytes"},"source_id":"2102.13004","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:81e620e8f0ff89ba458b51e360ca5bd8bd4f75b805b7284de53386197a7e446f","sha256:af3c889b8c19c670e9844022deb3cee4417ebf4439675bd0bf483c14a78cc451"],"state_sha256":"f030b9433f6a579de241a1d5bf6fb827c8a0683f49788d0060caff32b570fead"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WJuP0FGtNyQmsVycVZQhspUyEnOBIXpstGN0V/1wPWlP6E3muSVrrZg9Bjtc+eODJpspqfhqzsHVAI8myPx4CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T18:05:41.698966Z","bundle_sha256":"032d66656ce16deaa396e6febb565ad88b4ea2a10317f64e090160e9b95d10c4"}}