{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:NEKVXRZKGBILZBTBGUHUERTB5E","short_pith_number":"pith:NEKVXRZK","canonical_record":{"source":{"id":"2304.06034","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CY","submitted_at":"2023-03-30T05:29:13Z","cross_cats_sorted":["cs.AI","cs.CL","cs.CV"],"title_canon_sha256":"b77aec13cd66158441a9835b41160e2aaa5e4c43832072f62ace1cdc6b0909f5","abstract_canon_sha256":"b7cb2380eb1573e62379c212c440237681c7e873d777d79c317271a1da6fbc76"},"schema_version":"1.0"},"canonical_sha256":"69155bc72a3050bc8661350f424661e92faa22f717fbdcf5b80cc40077ef965a","source":{"kind":"arxiv","id":"2304.06034","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2304.06034","created_at":"2026-07-05T06:00:37Z"},{"alias_kind":"arxiv_version","alias_value":"2304.06034v1","created_at":"2026-07-05T06:00:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.06034","created_at":"2026-07-05T06:00:37Z"},{"alias_kind":"pith_short_12","alias_value":"NEKVXRZKGBIL","created_at":"2026-07-05T06:00:37Z"},{"alias_kind":"pith_short_16","alias_value":"NEKVXRZKGBILZBTB","created_at":"2026-07-05T06:00:37Z"},{"alias_kind":"pith_short_8","alias_value":"NEKVXRZK","created_at":"2026-07-05T06:00:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:NEKVXRZKGBILZBTBGUHUERTB5E","target":"record","payload":{"canonical_record":{"source":{"id":"2304.06034","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CY","submitted_at":"2023-03-30T05:29:13Z","cross_cats_sorted":["cs.AI","cs.CL","cs.CV"],"title_canon_sha256":"b77aec13cd66158441a9835b41160e2aaa5e4c43832072f62ace1cdc6b0909f5","abstract_canon_sha256":"b7cb2380eb1573e62379c212c440237681c7e873d777d79c317271a1da6fbc76"},"schema_version":"1.0"},"canonical_sha256":"69155bc72a3050bc8661350f424661e92faa22f717fbdcf5b80cc40077ef965a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:00:37.928895Z","signature_b64":"IRtAkPdGXhnC57nlC/s6C2Co2ri8nvFjEKTfO+rO5ejuBbr4qtG3iQ9RoGWHtvHo6UWX9gjXMdhP7jNKYmx+AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"69155bc72a3050bc8661350f424661e92faa22f717fbdcf5b80cc40077ef965a","last_reissued_at":"2026-07-05T06:00:37.928477Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:00:37.928477Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2304.06034","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":"2026-07-05T06:00:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zX8n5ORWXBfURizZBLkUR9w7/MkBMydnpOyQm/aSZoazIRd9lnx6Wnvx+Y/54RIC3BqlWaS3cR+RSLxyVq0fCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T20:44:58.210880Z"},"content_sha256":"c3d717f5264c2827db62e1e9fd4ded95a386fe32c0a5ef1c12390ac30ed78b4c","schema_version":"1.0","event_id":"sha256:c3d717f5264c2827db62e1e9fd4ded95a386fe32c0a5ef1c12390ac30ed78b4c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:NEKVXRZKGBILZBTBGUHUERTB5E","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Social Biases through the Text-to-Image Generation Lens","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.CV"],"primary_cat":"cs.CY","authors_text":"Besmira Nushi, Ranjita Naik","submitted_at":"2023-03-30T05:29:13Z","abstract_excerpt":"Text-to-Image (T2I) generation is enabling new applications that support creators, designers, and general end users of productivity software by generating illustrative content with high photorealism starting from a given descriptive text as a prompt. Such models are however trained on massive amounts of web data, which surfaces the peril of potential harmful biases that may leak in the generation process itself. In this paper, we take a multi-dimensional approach to studying and quantifying common social biases as reflected in the generated images, by focusing on how occupations, personality t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.06034","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/2304.06034/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":"2026-07-05T06:00:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d1oLH4UnCjvZ6OO3K7hdden+l73GDORM+lN1siOTnTf1aVUjY7sPTR0B0AYnLnc/OizOyqTBEPlRolwmAv+wDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T20:44:58.211424Z"},"content_sha256":"365afec068c265c56cb2df309fba79cf647a448d01db885e94a79e1946cbc21e","schema_version":"1.0","event_id":"sha256:365afec068c265c56cb2df309fba79cf647a448d01db885e94a79e1946cbc21e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NEKVXRZKGBILZBTBGUHUERTB5E/bundle.json","state_url":"https://pith.science/pith/NEKVXRZKGBILZBTBGUHUERTB5E/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NEKVXRZKGBILZBTBGUHUERTB5E/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":"2026-08-08T20:44:58Z","links":{"resolver":"https://pith.science/pith/NEKVXRZKGBILZBTBGUHUERTB5E","bundle":"https://pith.science/pith/NEKVXRZKGBILZBTBGUHUERTB5E/bundle.json","state":"https://pith.science/pith/NEKVXRZKGBILZBTBGUHUERTB5E/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NEKVXRZKGBILZBTBGUHUERTB5E/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:NEKVXRZKGBILZBTBGUHUERTB5E","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":"b7cb2380eb1573e62379c212c440237681c7e873d777d79c317271a1da6fbc76","cross_cats_sorted":["cs.AI","cs.CL","cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CY","submitted_at":"2023-03-30T05:29:13Z","title_canon_sha256":"b77aec13cd66158441a9835b41160e2aaa5e4c43832072f62ace1cdc6b0909f5"},"schema_version":"1.0","source":{"id":"2304.06034","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2304.06034","created_at":"2026-07-05T06:00:37Z"},{"alias_kind":"arxiv_version","alias_value":"2304.06034v1","created_at":"2026-07-05T06:00:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.06034","created_at":"2026-07-05T06:00:37Z"},{"alias_kind":"pith_short_12","alias_value":"NEKVXRZKGBIL","created_at":"2026-07-05T06:00:37Z"},{"alias_kind":"pith_short_16","alias_value":"NEKVXRZKGBILZBTB","created_at":"2026-07-05T06:00:37Z"},{"alias_kind":"pith_short_8","alias_value":"NEKVXRZK","created_at":"2026-07-05T06:00:37Z"}],"graph_snapshots":[{"event_id":"sha256:365afec068c265c56cb2df309fba79cf647a448d01db885e94a79e1946cbc21e","target":"graph","created_at":"2026-07-05T06:00:37Z","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/2304.06034/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Text-to-Image (T2I) generation is enabling new applications that support creators, designers, and general end users of productivity software by generating illustrative content with high photorealism starting from a given descriptive text as a prompt. Such models are however trained on massive amounts of web data, which surfaces the peril of potential harmful biases that may leak in the generation process itself. In this paper, we take a multi-dimensional approach to studying and quantifying common social biases as reflected in the generated images, by focusing on how occupations, personality t","authors_text":"Besmira Nushi, Ranjita Naik","cross_cats":["cs.AI","cs.CL","cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CY","submitted_at":"2023-03-30T05:29:13Z","title":"Social Biases through the Text-to-Image Generation Lens"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.06034","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:c3d717f5264c2827db62e1e9fd4ded95a386fe32c0a5ef1c12390ac30ed78b4c","target":"record","created_at":"2026-07-05T06:00:37Z","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":"b7cb2380eb1573e62379c212c440237681c7e873d777d79c317271a1da6fbc76","cross_cats_sorted":["cs.AI","cs.CL","cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CY","submitted_at":"2023-03-30T05:29:13Z","title_canon_sha256":"b77aec13cd66158441a9835b41160e2aaa5e4c43832072f62ace1cdc6b0909f5"},"schema_version":"1.0","source":{"id":"2304.06034","kind":"arxiv","version":1}},"canonical_sha256":"69155bc72a3050bc8661350f424661e92faa22f717fbdcf5b80cc40077ef965a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"69155bc72a3050bc8661350f424661e92faa22f717fbdcf5b80cc40077ef965a","first_computed_at":"2026-07-05T06:00:37.928477Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:00:37.928477Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"IRtAkPdGXhnC57nlC/s6C2Co2ri8nvFjEKTfO+rO5ejuBbr4qtG3iQ9RoGWHtvHo6UWX9gjXMdhP7jNKYmx+AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:00:37.928895Z","signed_message":"canonical_sha256_bytes"},"source_id":"2304.06034","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c3d717f5264c2827db62e1e9fd4ded95a386fe32c0a5ef1c12390ac30ed78b4c","sha256:365afec068c265c56cb2df309fba79cf647a448d01db885e94a79e1946cbc21e"],"state_sha256":"35d05945ee5496e5736e87bcdca1f6c54191e8f0ce435a1c2c79f2b11b8616ff"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PegC0S9UFLtNjjEiQHwJL16T2/6y3smRdPkXeJON4MiBic8+LEsWNvzufJ+SBqjcWrichKOKRlyucP+axM73Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T20:44:58.214751Z","bundle_sha256":"498b580a8d02ad6e9eb3a156e90bef5d92f3a11c6c49e9deeb365472f14c1da5"}}