{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:34X4O5J22DZSBKCVQF3CQKS5JR","short_pith_number":"pith:34X4O5J2","schema_version":"1.0","canonical_sha256":"df2fc7753ad0f320a8558176282a5d4c5533b79b314b5da4a6445897c2022465","source":{"kind":"arxiv","id":"2305.09300","version":2},"attestation_state":"computed","paper":{"title":"QB4AIRA: A Question Bank for AI Risk Assessment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Boming Xia, Harsha Perera, Jon Whittle, Liming Zhu, Olivier Salvado, Qinghua Lu, Sung Une Lee, Yue Liu","submitted_at":"2023-05-16T09:18:44Z","abstract_excerpt":"The rapid advancement of Artificial Intelligence (AI), represented by ChatGPT, has raised concerns about responsible AI development and utilization. Existing frameworks lack a comprehensive synthesis of AI risk assessment questions. To address this, we introduce QB4AIRA, a novel question bank developed by refining questions from five globally recognized AI risk frameworks, categorized according to Australia's AI ethics principles. QB4AIRA comprises 293 prioritized questions covering a wide range of AI risk areas, facilitating effective risk assessment. It serves as a valuable resource for stak"},"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":"2305.09300","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2023-05-16T09:18:44Z","cross_cats_sorted":[],"title_canon_sha256":"eea9b379b4e07f1d6dd2d595ff69e4956ed63de9cd27a1c021b12ab64f7953b8","abstract_canon_sha256":"1a87e65f066198294ef37f18c25458953cc72ad086af4eddd01cdf4bb263de58"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:29:21.005440Z","signature_b64":"kMWB8kYaPnxzAh/Zlqr9JxX2hSPizMMqD3UhfHAc21vNOmcE4P3bv1nStoYzIxuTFIweNKf1QMqo9rEXzjQGBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df2fc7753ad0f320a8558176282a5d4c5533b79b314b5da4a6445897c2022465","last_reissued_at":"2026-07-05T06:29:21.004939Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:29:21.004939Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"QB4AIRA: A Question Bank for AI Risk Assessment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Boming Xia, Harsha Perera, Jon Whittle, Liming Zhu, Olivier Salvado, Qinghua Lu, Sung Une Lee, Yue Liu","submitted_at":"2023-05-16T09:18:44Z","abstract_excerpt":"The rapid advancement of Artificial Intelligence (AI), represented by ChatGPT, has raised concerns about responsible AI development and utilization. Existing frameworks lack a comprehensive synthesis of AI risk assessment questions. To address this, we introduce QB4AIRA, a novel question bank developed by refining questions from five globally recognized AI risk frameworks, categorized according to Australia's AI ethics principles. QB4AIRA comprises 293 prioritized questions covering a wide range of AI risk areas, facilitating effective risk assessment. It serves as a valuable resource for stak"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.09300","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/2305.09300/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":"2305.09300","created_at":"2026-07-05T06:29:21.005015+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.09300v2","created_at":"2026-07-05T06:29:21.005015+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.09300","created_at":"2026-07-05T06:29:21.005015+00:00"},{"alias_kind":"pith_short_12","alias_value":"34X4O5J22DZS","created_at":"2026-07-05T06:29:21.005015+00:00"},{"alias_kind":"pith_short_16","alias_value":"34X4O5J22DZSBKCV","created_at":"2026-07-05T06:29:21.005015+00:00"},{"alias_kind":"pith_short_8","alias_value":"34X4O5J2","created_at":"2026-07-05T06:29:21.005015+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.18538","citing_title":"A Question Bank to Assess AI Inclusivity: Mapping out the Journey from Diversity Errors to Inclusion Excellence","ref_index":16,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/34X4O5J22DZSBKCVQF3CQKS5JR","json":"https://pith.science/pith/34X4O5J22DZSBKCVQF3CQKS5JR.json","graph_json":"https://pith.science/api/pith-number/34X4O5J22DZSBKCVQF3CQKS5JR/graph.json","events_json":"https://pith.science/api/pith-number/34X4O5J22DZSBKCVQF3CQKS5JR/events.json","paper":"https://pith.science/paper/34X4O5J2"},"agent_actions":{"view_html":"https://pith.science/pith/34X4O5J22DZSBKCVQF3CQKS5JR","download_json":"https://pith.science/pith/34X4O5J22DZSBKCVQF3CQKS5JR.json","view_paper":"https://pith.science/paper/34X4O5J2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.09300&json=true","fetch_graph":"https://pith.science/api/pith-number/34X4O5J22DZSBKCVQF3CQKS5JR/graph.json","fetch_events":"https://pith.science/api/pith-number/34X4O5J22DZSBKCVQF3CQKS5JR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/34X4O5J22DZSBKCVQF3CQKS5JR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/34X4O5J22DZSBKCVQF3CQKS5JR/action/storage_attestation","attest_author":"https://pith.science/pith/34X4O5J22DZSBKCVQF3CQKS5JR/action/author_attestation","sign_citation":"https://pith.science/pith/34X4O5J22DZSBKCVQF3CQKS5JR/action/citation_signature","submit_replication":"https://pith.science/pith/34X4O5J22DZSBKCVQF3CQKS5JR/action/replication_record"}},"created_at":"2026-07-05T06:29:21.005015+00:00","updated_at":"2026-07-05T06:29:21.005015+00:00"}