{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:W3H2HHRXUWWXLMRF3F5HRCALSX","short_pith_number":"pith:W3H2HHRX","schema_version":"1.0","canonical_sha256":"b6cfa39e37a5ad75b225d97a78880b95dd333ebcaeb21a3e21885ac0b5072b2c","source":{"kind":"arxiv","id":"2301.08488","version":1},"attestation_state":"computed","paper":{"title":"Towards Openness Beyond Open Access: User Journeys through 3 Open AI Collaboratives","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CY","authors_text":"Anne Lee Steele, Christopher Akiki, Jennifer Ding, Temi Popo, Yacine Jernite","submitted_at":"2023-01-20T09:34:59Z","abstract_excerpt":"Open Artificial Intelligence (Open source AI) collaboratives offer alternative pathways for how AI can be developed beyond well-resourced technology companies and who can be a part of the process. To understand how and why they work and what additionality they bring to the landscape, we focus on three such communities, each focused on a different kind of activity around AI: building models (BigScience workshop), tools and ways of working (The Turing Way), and ecosystems (Mozilla Festival's Building Trustworthy AI Working Group). First, we document the community structures that facilitate these"},"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":"2301.08488","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CY","submitted_at":"2023-01-20T09:34:59Z","cross_cats_sorted":[],"title_canon_sha256":"cb5b7433fd13af64d1b71f5d375c52827cd918a8dbbeff5898735d45b97745f7","abstract_canon_sha256":"ce87b65922ccfbe871ada3188595048cf3be64f7fcb089524fb29b5fe2d18aab"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:34:36.659174Z","signature_b64":"U83XMlS3V1GO007RfXvpM81c/EkQYr9MEortiM2W75z+THnVmn7rFGBgctQh2T8l13c4V+FqnSlLMySpXEy0Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b6cfa39e37a5ad75b225d97a78880b95dd333ebcaeb21a3e21885ac0b5072b2c","last_reissued_at":"2026-07-05T05:34:36.658755Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:34:36.658755Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Openness Beyond Open Access: User Journeys through 3 Open AI Collaboratives","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CY","authors_text":"Anne Lee Steele, Christopher Akiki, Jennifer Ding, Temi Popo, Yacine Jernite","submitted_at":"2023-01-20T09:34:59Z","abstract_excerpt":"Open Artificial Intelligence (Open source AI) collaboratives offer alternative pathways for how AI can be developed beyond well-resourced technology companies and who can be a part of the process. To understand how and why they work and what additionality they bring to the landscape, we focus on three such communities, each focused on a different kind of activity around AI: building models (BigScience workshop), tools and ways of working (The Turing Way), and ecosystems (Mozilla Festival's Building Trustworthy AI Working Group). First, we document the community structures that facilitate these"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.08488","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/2301.08488/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":"2301.08488","created_at":"2026-07-05T05:34:36.658812+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.08488v1","created_at":"2026-07-05T05:34:36.658812+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.08488","created_at":"2026-07-05T05:34:36.658812+00:00"},{"alias_kind":"pith_short_12","alias_value":"W3H2HHRXUWWX","created_at":"2026-07-05T05:34:36.658812+00:00"},{"alias_kind":"pith_short_16","alias_value":"W3H2HHRXUWWXLMRF","created_at":"2026-07-05T05:34:36.658812+00:00"},{"alias_kind":"pith_short_8","alias_value":"W3H2HHRX","created_at":"2026-07-05T05:34:36.658812+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.10272","citing_title":"Collective Bargaining in the Information Economy Can Address AI-Driven Power Concentration","ref_index":15,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/W3H2HHRXUWWXLMRF3F5HRCALSX","json":"https://pith.science/pith/W3H2HHRXUWWXLMRF3F5HRCALSX.json","graph_json":"https://pith.science/api/pith-number/W3H2HHRXUWWXLMRF3F5HRCALSX/graph.json","events_json":"https://pith.science/api/pith-number/W3H2HHRXUWWXLMRF3F5HRCALSX/events.json","paper":"https://pith.science/paper/W3H2HHRX"},"agent_actions":{"view_html":"https://pith.science/pith/W3H2HHRXUWWXLMRF3F5HRCALSX","download_json":"https://pith.science/pith/W3H2HHRXUWWXLMRF3F5HRCALSX.json","view_paper":"https://pith.science/paper/W3H2HHRX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.08488&json=true","fetch_graph":"https://pith.science/api/pith-number/W3H2HHRXUWWXLMRF3F5HRCALSX/graph.json","fetch_events":"https://pith.science/api/pith-number/W3H2HHRXUWWXLMRF3F5HRCALSX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W3H2HHRXUWWXLMRF3F5HRCALSX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W3H2HHRXUWWXLMRF3F5HRCALSX/action/storage_attestation","attest_author":"https://pith.science/pith/W3H2HHRXUWWXLMRF3F5HRCALSX/action/author_attestation","sign_citation":"https://pith.science/pith/W3H2HHRXUWWXLMRF3F5HRCALSX/action/citation_signature","submit_replication":"https://pith.science/pith/W3H2HHRXUWWXLMRF3F5HRCALSX/action/replication_record"}},"created_at":"2026-07-05T05:34:36.658812+00:00","updated_at":"2026-07-05T05:34:36.658812+00:00"}