{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:JVH3WU7S54J3SSV4QY54Q7GAFX","short_pith_number":"pith:JVH3WU7S","schema_version":"1.0","canonical_sha256":"4d4fbb53f2ef13b94abc863bc87cc02df04fb7ec6afbababa3a0ad3393742c8c","source":{"kind":"arxiv","id":"2306.03097","version":1},"attestation_state":"computed","paper":{"title":"Seeing Seeds Beyond Weeds: Green Teaming Generative AI for Beneficial Uses","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.HC","authors_text":"Haiyi Zhu, Jordan Taylor, Logan Stapleton, Sarah Fox, Tongshuang Wu","submitted_at":"2023-05-30T21:52:33Z","abstract_excerpt":"Large generative AI models (GMs) like GPT and DALL-E are trained to generate content for general, wide-ranging purposes. GM content filters are generalized to filter out content which has a risk of harm in many cases, e.g., hate speech. However, prohibited content is not always harmful -- there are instances where generating prohibited content can be beneficial. So, when GMs filter out content, they preclude beneficial use cases along with harmful ones. Which use cases are precluded reflects the values embedded in GM content filtering. Recent work on red teaming proposes methods to bypass GM c"},"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":"2306.03097","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2023-05-30T21:52:33Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"436b3001a80fb271b07ee3ef08a7e9769f0c83430f3d7b84524e740712c94bdf","abstract_canon_sha256":"0139a7e4c805d3d78fae01e6efaa07a94fb3feddbcdaecfa2402a920eb48d0ce"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:17:53.665440Z","signature_b64":"j583dun/Uj34xX9uuInNTurHP1VqqbTXc/2zyedgNRwfhfMSHcCaW/tkFfic8DOi24gY5x4O4wih7YTRc7l1Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4d4fbb53f2ef13b94abc863bc87cc02df04fb7ec6afbababa3a0ad3393742c8c","last_reissued_at":"2026-07-05T06:17:53.664892Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:17:53.664892Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Seeing Seeds Beyond Weeds: Green Teaming Generative AI for Beneficial Uses","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.HC","authors_text":"Haiyi Zhu, Jordan Taylor, Logan Stapleton, Sarah Fox, Tongshuang Wu","submitted_at":"2023-05-30T21:52:33Z","abstract_excerpt":"Large generative AI models (GMs) like GPT and DALL-E are trained to generate content for general, wide-ranging purposes. GM content filters are generalized to filter out content which has a risk of harm in many cases, e.g., hate speech. However, prohibited content is not always harmful -- there are instances where generating prohibited content can be beneficial. So, when GMs filter out content, they preclude beneficial use cases along with harmful ones. Which use cases are precluded reflects the values embedded in GM content filtering. Recent work on red teaming proposes methods to bypass GM c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.03097","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/2306.03097/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":"2306.03097","created_at":"2026-07-05T06:17:53.664959+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.03097v1","created_at":"2026-07-05T06:17:53.664959+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.03097","created_at":"2026-07-05T06:17:53.664959+00:00"},{"alias_kind":"pith_short_12","alias_value":"JVH3WU7S54J3","created_at":"2026-07-05T06:17:53.664959+00:00"},{"alias_kind":"pith_short_16","alias_value":"JVH3WU7S54J3SSV4","created_at":"2026-07-05T06:17:53.664959+00:00"},{"alias_kind":"pith_short_8","alias_value":"JVH3WU7S","created_at":"2026-07-05T06:17:53.664959+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JVH3WU7S54J3SSV4QY54Q7GAFX","json":"https://pith.science/pith/JVH3WU7S54J3SSV4QY54Q7GAFX.json","graph_json":"https://pith.science/api/pith-number/JVH3WU7S54J3SSV4QY54Q7GAFX/graph.json","events_json":"https://pith.science/api/pith-number/JVH3WU7S54J3SSV4QY54Q7GAFX/events.json","paper":"https://pith.science/paper/JVH3WU7S"},"agent_actions":{"view_html":"https://pith.science/pith/JVH3WU7S54J3SSV4QY54Q7GAFX","download_json":"https://pith.science/pith/JVH3WU7S54J3SSV4QY54Q7GAFX.json","view_paper":"https://pith.science/paper/JVH3WU7S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.03097&json=true","fetch_graph":"https://pith.science/api/pith-number/JVH3WU7S54J3SSV4QY54Q7GAFX/graph.json","fetch_events":"https://pith.science/api/pith-number/JVH3WU7S54J3SSV4QY54Q7GAFX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JVH3WU7S54J3SSV4QY54Q7GAFX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JVH3WU7S54J3SSV4QY54Q7GAFX/action/storage_attestation","attest_author":"https://pith.science/pith/JVH3WU7S54J3SSV4QY54Q7GAFX/action/author_attestation","sign_citation":"https://pith.science/pith/JVH3WU7S54J3SSV4QY54Q7GAFX/action/citation_signature","submit_replication":"https://pith.science/pith/JVH3WU7S54J3SSV4QY54Q7GAFX/action/replication_record"}},"created_at":"2026-07-05T06:17:53.664959+00:00","updated_at":"2026-07-05T06:17:53.664959+00:00"}