{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:C5FNMLZ3VMSYEEU4MV4PCDJPWG","short_pith_number":"pith:C5FNMLZ3","canonical_record":{"source":{"id":"2212.02648","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-12-05T23:15:43Z","cross_cats_sorted":["cs.AI","cs.HC","cs.LG"],"title_canon_sha256":"91fa4e425bafbc389d5992fd57a78841247c12d4e722060cbe59c2055829f76d","abstract_canon_sha256":"daf9dafa660691183366c0ec1e5e4701bec9c1e968a34452178318dee44e8ff2"},"schema_version":"1.0"},"canonical_sha256":"174ad62f3bab2582129c6578f10d2fb18f356e72369d7763bcd08f260febbed4","source":{"kind":"arxiv","id":"2212.02648","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.02648","created_at":"2026-07-05T07:07:05Z"},{"alias_kind":"arxiv_version","alias_value":"2212.02648v3","created_at":"2026-07-05T07:07:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.02648","created_at":"2026-07-05T07:07:05Z"},{"alias_kind":"pith_short_12","alias_value":"C5FNMLZ3VMSY","created_at":"2026-07-05T07:07:05Z"},{"alias_kind":"pith_short_16","alias_value":"C5FNMLZ3VMSYEEU4","created_at":"2026-07-05T07:07:05Z"},{"alias_kind":"pith_short_8","alias_value":"C5FNMLZ3","created_at":"2026-07-05T07:07:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:C5FNMLZ3VMSYEEU4MV4PCDJPWG","target":"record","payload":{"canonical_record":{"source":{"id":"2212.02648","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-12-05T23:15:43Z","cross_cats_sorted":["cs.AI","cs.HC","cs.LG"],"title_canon_sha256":"91fa4e425bafbc389d5992fd57a78841247c12d4e722060cbe59c2055829f76d","abstract_canon_sha256":"daf9dafa660691183366c0ec1e5e4701bec9c1e968a34452178318dee44e8ff2"},"schema_version":"1.0"},"canonical_sha256":"174ad62f3bab2582129c6578f10d2fb18f356e72369d7763bcd08f260febbed4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:07:05.679849Z","signature_b64":"kvRyDzyaLWkAmk6+ziFlffUer/AWze5QdtahQ15UqtaVVIjxlF1QX+vlu7EqvOF134KBJvPuPgbubZBjUZ2fBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"174ad62f3bab2582129c6578f10d2fb18f356e72369d7763bcd08f260febbed4","last_reissued_at":"2026-07-05T07:07:05.679366Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:07:05.679366Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2212.02648","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:07:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wOAo9zmwSGBfhcMqxyKXQXpLxMrnIzQMGtrSQP85KlZMztXmZhDJw2CNozjBOwCEkwp7sQHBlNQKd3RvdIgXBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T20:57:51.405053Z"},"content_sha256":"b0ef73a8dac6fbca3d47a56a9b73ae1fbb7cbb53ae958a207ba0546cb9e9a393","schema_version":"1.0","event_id":"sha256:b0ef73a8dac6fbca3d47a56a9b73ae1fbb7cbb53ae958a207ba0546cb9e9a393"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:C5FNMLZ3VMSYEEU4MV4PCDJPWG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Spuriosity Rankings: Sorting Data to Measure and Mitigate Biases","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.HC","cs.LG"],"primary_cat":"cs.CV","authors_text":"Mazda Moayeri, Sahil Singla, Soheil Feizi, Wenxiao Wang","submitted_at":"2022-12-05T23:15:43Z","abstract_excerpt":"We present a simple but effective method to measure and mitigate model biases caused by reliance on spurious cues. Instead of requiring costly changes to one's data or model training, our method better utilizes the data one already has by sorting them. Specifically, we rank images within their classes based on spuriosity (the degree to which common spurious cues are present), proxied via deep neural features of an interpretable network. With spuriosity rankings, it is easy to identify minority subpopulations (i.e. low spuriosity images) and assess model bias as the gap in accuracy between high"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.02648","kind":"arxiv","version":3},"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/2212.02648/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":"2026-07-05T07:07:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"H84SqCngiDeTOMFXSbs/pEMhf2RajPKb2N4XFU0QcCKSrrxiIIoGmZvVWzchJBi7BH/bSdJAS4opux6I77hcBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T20:57:51.405462Z"},"content_sha256":"44e375fde6ac8b20e33ba7c8ae2c7bbbd3505a3d4a9b3d1705f6a3846ac21392","schema_version":"1.0","event_id":"sha256:44e375fde6ac8b20e33ba7c8ae2c7bbbd3505a3d4a9b3d1705f6a3846ac21392"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/C5FNMLZ3VMSYEEU4MV4PCDJPWG/bundle.json","state_url":"https://pith.science/pith/C5FNMLZ3VMSYEEU4MV4PCDJPWG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/C5FNMLZ3VMSYEEU4MV4PCDJPWG/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":"2026-07-06T20:57:51Z","links":{"resolver":"https://pith.science/pith/C5FNMLZ3VMSYEEU4MV4PCDJPWG","bundle":"https://pith.science/pith/C5FNMLZ3VMSYEEU4MV4PCDJPWG/bundle.json","state":"https://pith.science/pith/C5FNMLZ3VMSYEEU4MV4PCDJPWG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/C5FNMLZ3VMSYEEU4MV4PCDJPWG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:C5FNMLZ3VMSYEEU4MV4PCDJPWG","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":"daf9dafa660691183366c0ec1e5e4701bec9c1e968a34452178318dee44e8ff2","cross_cats_sorted":["cs.AI","cs.HC","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-12-05T23:15:43Z","title_canon_sha256":"91fa4e425bafbc389d5992fd57a78841247c12d4e722060cbe59c2055829f76d"},"schema_version":"1.0","source":{"id":"2212.02648","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.02648","created_at":"2026-07-05T07:07:05Z"},{"alias_kind":"arxiv_version","alias_value":"2212.02648v3","created_at":"2026-07-05T07:07:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.02648","created_at":"2026-07-05T07:07:05Z"},{"alias_kind":"pith_short_12","alias_value":"C5FNMLZ3VMSY","created_at":"2026-07-05T07:07:05Z"},{"alias_kind":"pith_short_16","alias_value":"C5FNMLZ3VMSYEEU4","created_at":"2026-07-05T07:07:05Z"},{"alias_kind":"pith_short_8","alias_value":"C5FNMLZ3","created_at":"2026-07-05T07:07:05Z"}],"graph_snapshots":[{"event_id":"sha256:44e375fde6ac8b20e33ba7c8ae2c7bbbd3505a3d4a9b3d1705f6a3846ac21392","target":"graph","created_at":"2026-07-05T07:07:05Z","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/2212.02648/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present a simple but effective method to measure and mitigate model biases caused by reliance on spurious cues. Instead of requiring costly changes to one's data or model training, our method better utilizes the data one already has by sorting them. Specifically, we rank images within their classes based on spuriosity (the degree to which common spurious cues are present), proxied via deep neural features of an interpretable network. With spuriosity rankings, it is easy to identify minority subpopulations (i.e. low spuriosity images) and assess model bias as the gap in accuracy between high","authors_text":"Mazda Moayeri, Sahil Singla, Soheil Feizi, Wenxiao Wang","cross_cats":["cs.AI","cs.HC","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-12-05T23:15:43Z","title":"Spuriosity Rankings: Sorting Data to Measure and Mitigate Biases"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.02648","kind":"arxiv","version":3},"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:b0ef73a8dac6fbca3d47a56a9b73ae1fbb7cbb53ae958a207ba0546cb9e9a393","target":"record","created_at":"2026-07-05T07:07:05Z","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":"daf9dafa660691183366c0ec1e5e4701bec9c1e968a34452178318dee44e8ff2","cross_cats_sorted":["cs.AI","cs.HC","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-12-05T23:15:43Z","title_canon_sha256":"91fa4e425bafbc389d5992fd57a78841247c12d4e722060cbe59c2055829f76d"},"schema_version":"1.0","source":{"id":"2212.02648","kind":"arxiv","version":3}},"canonical_sha256":"174ad62f3bab2582129c6578f10d2fb18f356e72369d7763bcd08f260febbed4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"174ad62f3bab2582129c6578f10d2fb18f356e72369d7763bcd08f260febbed4","first_computed_at":"2026-07-05T07:07:05.679366Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:07:05.679366Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kvRyDzyaLWkAmk6+ziFlffUer/AWze5QdtahQ15UqtaVVIjxlF1QX+vlu7EqvOF134KBJvPuPgbubZBjUZ2fBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:07:05.679849Z","signed_message":"canonical_sha256_bytes"},"source_id":"2212.02648","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b0ef73a8dac6fbca3d47a56a9b73ae1fbb7cbb53ae958a207ba0546cb9e9a393","sha256:44e375fde6ac8b20e33ba7c8ae2c7bbbd3505a3d4a9b3d1705f6a3846ac21392"],"state_sha256":"919825321236ec8184f26bdf0f4f8bc982e248d4546e85dc697268fd8af3e3e0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LmMeFVkQ6lwGMS16ARI4xWmbte/K3M1wO8StmysVSLWFy/9f6XPgfCXUizfgcm9SJsb/lkvujONtFK6uxYwhDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T20:57:51.407444Z","bundle_sha256":"e969c9eded6f0c3c9a97d4eb57c0bdbd0c57116e61d791341f02fa0042d8bbb2"}}