{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:EPWHRHJ7UNVFBUWPRUEPC4D7CY","short_pith_number":"pith:EPWHRHJ7","schema_version":"1.0","canonical_sha256":"23ec789d3fa36a50d2cf8d08f1707f16063550381a37336864eb8bf21cda12de","source":{"kind":"arxiv","id":"2211.03759","version":2},"attestation_state":"computed","paper":{"title":"Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.CL","authors_text":"Aylin Caliskan, Dan Jurafsky, Debora Nozza, Esin Durmus, Faisal Ladhak, Federico Bianchi, James Zou, Myra Cheng, Pratyusha Kalluri, Tatsunori Hashimoto","submitted_at":"2022-11-07T18:31:07Z","abstract_excerpt":"Machine learning models that convert user-written text descriptions into images are now widely available online and used by millions of users to generate millions of images a day. We investigate the potential for these models to amplify dangerous and complex stereotypes. We find a broad range of ordinary prompts produce stereotypes, including prompts simply mentioning traits, descriptors, occupations, or objects. For example, we find cases of prompting for basic traits or social roles resulting in images reinforcing whiteness as ideal, prompting for occupations resulting in amplification of ra"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2211.03759","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-11-07T18:31:07Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"4ba33c8bad29e76e19f7f20bf3b2eb23f884d856a0f87bafc5442b235c053243","abstract_canon_sha256":"04f6696167afb34e914e403ff825a0cc2a8d6b821606ab855cbea3bda97e5d28"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:18:18.545732Z","signature_b64":"9VEovMm618rgzg5ChFOVbodmz3JyKt6k/9kpV7yYLJ8D9A8uGqVxbRxjRowNqCW4W7Sz6lT7kNT03xeB7fNKAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"23ec789d3fa36a50d2cf8d08f1707f16063550381a37336864eb8bf21cda12de","last_reissued_at":"2026-07-05T06:18:18.545283Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:18:18.545283Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.CL","authors_text":"Aylin Caliskan, Dan Jurafsky, Debora Nozza, Esin Durmus, Faisal Ladhak, Federico Bianchi, James Zou, Myra Cheng, Pratyusha Kalluri, Tatsunori Hashimoto","submitted_at":"2022-11-07T18:31:07Z","abstract_excerpt":"Machine learning models that convert user-written text descriptions into images are now widely available online and used by millions of users to generate millions of images a day. We investigate the potential for these models to amplify dangerous and complex stereotypes. We find a broad range of ordinary prompts produce stereotypes, including prompts simply mentioning traits, descriptors, occupations, or objects. For example, we find cases of prompting for basic traits or social roles resulting in images reinforcing whiteness as ideal, prompting for occupations resulting in amplification of ra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.03759","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/2211.03759/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2211.03759","created_at":"2026-07-05T06:18:18.545352+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.03759v2","created_at":"2026-07-05T06:18:18.545352+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.03759","created_at":"2026-07-05T06:18:18.545352+00:00"},{"alias_kind":"pith_short_12","alias_value":"EPWHRHJ7UNVF","created_at":"2026-07-05T06:18:18.545352+00:00"},{"alias_kind":"pith_short_16","alias_value":"EPWHRHJ7UNVFBUWP","created_at":"2026-07-05T06:18:18.545352+00:00"},{"alias_kind":"pith_short_8","alias_value":"EPWHRHJ7","created_at":"2026-07-05T06:18:18.545352+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.20241","citing_title":"BAFIS: Dataset + Framework to assess occupational Bias and Human Preference in modern Text-to-image Models","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2604.21036","citing_title":"Who Defines Fairness? Target-Based Prompting for Demographic Representation in Generative Models","ref_index":3,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EPWHRHJ7UNVFBUWPRUEPC4D7CY","json":"https://pith.science/pith/EPWHRHJ7UNVFBUWPRUEPC4D7CY.json","graph_json":"https://pith.science/api/pith-number/EPWHRHJ7UNVFBUWPRUEPC4D7CY/graph.json","events_json":"https://pith.science/api/pith-number/EPWHRHJ7UNVFBUWPRUEPC4D7CY/events.json","paper":"https://pith.science/paper/EPWHRHJ7"},"agent_actions":{"view_html":"https://pith.science/pith/EPWHRHJ7UNVFBUWPRUEPC4D7CY","download_json":"https://pith.science/pith/EPWHRHJ7UNVFBUWPRUEPC4D7CY.json","view_paper":"https://pith.science/paper/EPWHRHJ7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.03759&json=true","fetch_graph":"https://pith.science/api/pith-number/EPWHRHJ7UNVFBUWPRUEPC4D7CY/graph.json","fetch_events":"https://pith.science/api/pith-number/EPWHRHJ7UNVFBUWPRUEPC4D7CY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EPWHRHJ7UNVFBUWPRUEPC4D7CY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EPWHRHJ7UNVFBUWPRUEPC4D7CY/action/storage_attestation","attest_author":"https://pith.science/pith/EPWHRHJ7UNVFBUWPRUEPC4D7CY/action/author_attestation","sign_citation":"https://pith.science/pith/EPWHRHJ7UNVFBUWPRUEPC4D7CY/action/citation_signature","submit_replication":"https://pith.science/pith/EPWHRHJ7UNVFBUWPRUEPC4D7CY/action/replication_record"}},"created_at":"2026-07-05T06:18:18.545352+00:00","updated_at":"2026-07-05T06:18:18.545352+00:00"}