{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:XXQ5V7AVB2YDI4NY7OZ3L6Z4B7","short_pith_number":"pith:XXQ5V7AV","canonical_record":{"source":{"id":"2211.02730","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-11-04T20:04:39Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4defb43d0addc8ccdad65b3bf8182396d9d5476d0e859816af3de8b511787945","abstract_canon_sha256":"1d503ff90404d27da5338f8cecec197cd14487f8fa8c1c6c7979bfa2473f44dc"},"schema_version":"1.0"},"canonical_sha256":"bde1dafc150eb03471b8fbb3b5fb3c0fd30ab8ef5f3b95f5c862d172bf47c105","source":{"kind":"arxiv","id":"2211.02730","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.02730","created_at":"2026-07-05T05:13:21Z"},{"alias_kind":"arxiv_version","alias_value":"2211.02730v1","created_at":"2026-07-05T05:13:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.02730","created_at":"2026-07-05T05:13:21Z"},{"alias_kind":"pith_short_12","alias_value":"XXQ5V7AVB2YD","created_at":"2026-07-05T05:13:21Z"},{"alias_kind":"pith_short_16","alias_value":"XXQ5V7AVB2YDI4NY","created_at":"2026-07-05T05:13:21Z"},{"alias_kind":"pith_short_8","alias_value":"XXQ5V7AV","created_at":"2026-07-05T05:13:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:XXQ5V7AVB2YDI4NY7OZ3L6Z4B7","target":"record","payload":{"canonical_record":{"source":{"id":"2211.02730","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-11-04T20:04:39Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4defb43d0addc8ccdad65b3bf8182396d9d5476d0e859816af3de8b511787945","abstract_canon_sha256":"1d503ff90404d27da5338f8cecec197cd14487f8fa8c1c6c7979bfa2473f44dc"},"schema_version":"1.0"},"canonical_sha256":"bde1dafc150eb03471b8fbb3b5fb3c0fd30ab8ef5f3b95f5c862d172bf47c105","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:13:21.082124Z","signature_b64":"Wsez/t6loNc+63Npjtwk3jKBFpw5VTk9F9AIjpndvSAyPlAgJdopibneSA7/cOIZP7IcYBlx4spjRfTXDf33DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bde1dafc150eb03471b8fbb3b5fb3c0fd30ab8ef5f3b95f5c862d172bf47c105","last_reissued_at":"2026-07-05T05:13:21.081613Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:13:21.081613Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2211.02730","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":"2026-07-05T05:13:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iVBKYqmjf0311voA/ycUGDjcq07MB5ZzkSjAQ2/4z7ISYszQa2ZpJLNOM04ak21y07rtlNu5J2jceBQvUQuaAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T19:41:44.237657Z"},"content_sha256":"814a6c26d8e7b3b09a051530b18b82e4e7d42bd1d19de4aca61862378ffcf0d9","schema_version":"1.0","event_id":"sha256:814a6c26d8e7b3b09a051530b18b82e4e7d42bd1d19de4aca61862378ffcf0d9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:XXQ5V7AVB2YDI4NY7OZ3L6Z4B7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Uncertainty-aware predictive modeling for fair data-driven decisions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Christoph Kern, David R\\\"ugamer, Patrick Kaiser","submitted_at":"2022-11-04T20:04:39Z","abstract_excerpt":"Both industry and academia have made considerable progress in developing trustworthy and responsible machine learning (ML) systems. While critical concepts like fairness and explainability are often addressed, the safety of systems is typically not sufficiently taken into account. By viewing data-driven decision systems as socio-technical systems, we draw on the uncertainty in ML literature to show how fairML systems can also be safeML systems. We posit that a fair model needs to be an uncertainty-aware model, e.g. by drawing on distributional regression. For fair decisions, we argue that a sa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.02730","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/2211.02730/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":"2026-07-05T05:13:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ozr6etLKX3l/byxG2ARjQ9z54el+pXcC1e8nDBWfgXQyYbHhTN5ilSBLajXRtcU74lTi76nrsPCRUGvc+4rlCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T19:41:44.238822Z"},"content_sha256":"2ae82b19fb8a411cacd6912aabe20d55fabea883a2090046992abe9821752972","schema_version":"1.0","event_id":"sha256:2ae82b19fb8a411cacd6912aabe20d55fabea883a2090046992abe9821752972"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XXQ5V7AVB2YDI4NY7OZ3L6Z4B7/bundle.json","state_url":"https://pith.science/pith/XXQ5V7AVB2YDI4NY7OZ3L6Z4B7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XXQ5V7AVB2YDI4NY7OZ3L6Z4B7/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":"2026-08-13T19:41:44Z","links":{"resolver":"https://pith.science/pith/XXQ5V7AVB2YDI4NY7OZ3L6Z4B7","bundle":"https://pith.science/pith/XXQ5V7AVB2YDI4NY7OZ3L6Z4B7/bundle.json","state":"https://pith.science/pith/XXQ5V7AVB2YDI4NY7OZ3L6Z4B7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XXQ5V7AVB2YDI4NY7OZ3L6Z4B7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:XXQ5V7AVB2YDI4NY7OZ3L6Z4B7","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":"1d503ff90404d27da5338f8cecec197cd14487f8fa8c1c6c7979bfa2473f44dc","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-11-04T20:04:39Z","title_canon_sha256":"4defb43d0addc8ccdad65b3bf8182396d9d5476d0e859816af3de8b511787945"},"schema_version":"1.0","source":{"id":"2211.02730","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.02730","created_at":"2026-07-05T05:13:21Z"},{"alias_kind":"arxiv_version","alias_value":"2211.02730v1","created_at":"2026-07-05T05:13:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.02730","created_at":"2026-07-05T05:13:21Z"},{"alias_kind":"pith_short_12","alias_value":"XXQ5V7AVB2YD","created_at":"2026-07-05T05:13:21Z"},{"alias_kind":"pith_short_16","alias_value":"XXQ5V7AVB2YDI4NY","created_at":"2026-07-05T05:13:21Z"},{"alias_kind":"pith_short_8","alias_value":"XXQ5V7AV","created_at":"2026-07-05T05:13:21Z"}],"graph_snapshots":[{"event_id":"sha256:2ae82b19fb8a411cacd6912aabe20d55fabea883a2090046992abe9821752972","target":"graph","created_at":"2026-07-05T05:13:21Z","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/2211.02730/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Both industry and academia have made considerable progress in developing trustworthy and responsible machine learning (ML) systems. While critical concepts like fairness and explainability are often addressed, the safety of systems is typically not sufficiently taken into account. By viewing data-driven decision systems as socio-technical systems, we draw on the uncertainty in ML literature to show how fairML systems can also be safeML systems. We posit that a fair model needs to be an uncertainty-aware model, e.g. by drawing on distributional regression. For fair decisions, we argue that a sa","authors_text":"Christoph Kern, David R\\\"ugamer, Patrick Kaiser","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-11-04T20:04:39Z","title":"Uncertainty-aware predictive modeling for fair data-driven decisions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.02730","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":"sha256:814a6c26d8e7b3b09a051530b18b82e4e7d42bd1d19de4aca61862378ffcf0d9","target":"record","created_at":"2026-07-05T05:13:21Z","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":"1d503ff90404d27da5338f8cecec197cd14487f8fa8c1c6c7979bfa2473f44dc","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-11-04T20:04:39Z","title_canon_sha256":"4defb43d0addc8ccdad65b3bf8182396d9d5476d0e859816af3de8b511787945"},"schema_version":"1.0","source":{"id":"2211.02730","kind":"arxiv","version":1}},"canonical_sha256":"bde1dafc150eb03471b8fbb3b5fb3c0fd30ab8ef5f3b95f5c862d172bf47c105","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bde1dafc150eb03471b8fbb3b5fb3c0fd30ab8ef5f3b95f5c862d172bf47c105","first_computed_at":"2026-07-05T05:13:21.081613Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:13:21.081613Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Wsez/t6loNc+63Npjtwk3jKBFpw5VTk9F9AIjpndvSAyPlAgJdopibneSA7/cOIZP7IcYBlx4spjRfTXDf33DA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:13:21.082124Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.02730","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:814a6c26d8e7b3b09a051530b18b82e4e7d42bd1d19de4aca61862378ffcf0d9","sha256:2ae82b19fb8a411cacd6912aabe20d55fabea883a2090046992abe9821752972"],"state_sha256":"b5366d4f7ceaf9a0d9d47fa12a576a61ba5696a740c2abb32cca922f5cef709d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4RNtp8mcwr/BbDOx6H5FP6vI7chlh42apbB0NoaT/ngIB6B3Id1fHSMxyZPTp9MFK9D8Q5vocCZYgLlbrLIIAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T19:41:44.276603Z","bundle_sha256":"86296c973de9b0196cce7e0a9afe4916bb3d71c4ce3427eb28d7ac7ead2d3891"}}