{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:CVLMU5PKXURSZT3U6KS5KMLITX","short_pith_number":"pith:CVLMU5PK","schema_version":"1.0","canonical_sha256":"1556ca75eabd232ccf74f2a5d531689dcb0235149c901738e9f79d805aa63933","source":{"kind":"arxiv","id":"2401.14462","version":1},"attestation_state":"computed","paper":{"title":"AI auditing: The Broken Bus on the Road to AI Accountability","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CY","authors_text":"Abeba Birhane, Briana Vecchione, Inioluwa Deborah Raji, Ryan Steed, Victor Ojewale","submitted_at":"2024-01-25T19:00:29Z","abstract_excerpt":"One of the most concrete measures to take towards meaningful AI accountability is to consequentially assess and report the systems' performance and impact. However, the practical nature of the \"AI audit\" ecosystem is muddled and imprecise, making it difficult to work through various concepts and map out the stakeholders involved in the practice. First, we taxonomize current AI audit practices as completed by regulators, law firms, civil society, journalism, academia, consulting agencies. Next, we assess the impact of audits done by stakeholders within each domain. We find that only a subset of"},"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":"2401.14462","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CY","submitted_at":"2024-01-25T19:00:29Z","cross_cats_sorted":[],"title_canon_sha256":"d20837efbdf41ade48e5d81d57dc20a090ee0e47e0d1ee3fac8dff93844b96dd","abstract_canon_sha256":"b43163f31c6c3ad28822b4f8a4c70b3927fc88c650fa645e444fdbecd2e2e207"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:37:56.647767Z","signature_b64":"A5qoxNO4NTJDL1DdFcCxkeJ9fOhnXhmxHtXmPVWy48vufFFaiZhwX7L0k0iu85tIeXsBLBYLzYIiYk0R0/xeDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1556ca75eabd232ccf74f2a5d531689dcb0235149c901738e9f79d805aa63933","last_reissued_at":"2026-07-05T07:37:56.647335Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:37:56.647335Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AI auditing: The Broken Bus on the Road to AI Accountability","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CY","authors_text":"Abeba Birhane, Briana Vecchione, Inioluwa Deborah Raji, Ryan Steed, Victor Ojewale","submitted_at":"2024-01-25T19:00:29Z","abstract_excerpt":"One of the most concrete measures to take towards meaningful AI accountability is to consequentially assess and report the systems' performance and impact. However, the practical nature of the \"AI audit\" ecosystem is muddled and imprecise, making it difficult to work through various concepts and map out the stakeholders involved in the practice. First, we taxonomize current AI audit practices as completed by regulators, law firms, civil society, journalism, academia, consulting agencies. Next, we assess the impact of audits done by stakeholders within each domain. We find that only a subset of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.14462","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/2401.14462/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":"2401.14462","created_at":"2026-07-05T07:37:56.647389+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.14462v1","created_at":"2026-07-05T07:37:56.647389+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.14462","created_at":"2026-07-05T07:37:56.647389+00:00"},{"alias_kind":"pith_short_12","alias_value":"CVLMU5PKXURS","created_at":"2026-07-05T07:37:56.647389+00:00"},{"alias_kind":"pith_short_16","alias_value":"CVLMU5PKXURSZT3U","created_at":"2026-07-05T07:37:56.647389+00:00"},{"alias_kind":"pith_short_8","alias_value":"CVLMU5PK","created_at":"2026-07-05T07:37:56.647389+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2602.04759","citing_title":"How to Stop Playing Whack-a-Mole: Mapping the Ecosystem of Technologies Facilitating AI-Generated Non-Consensual Intimate Images","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2605.17159","citing_title":"MADP: A Multi-Agent Pipeline for Sustainable Document Processing with Human-in-the-Loop","ref_index":3,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CVLMU5PKXURSZT3U6KS5KMLITX","json":"https://pith.science/pith/CVLMU5PKXURSZT3U6KS5KMLITX.json","graph_json":"https://pith.science/api/pith-number/CVLMU5PKXURSZT3U6KS5KMLITX/graph.json","events_json":"https://pith.science/api/pith-number/CVLMU5PKXURSZT3U6KS5KMLITX/events.json","paper":"https://pith.science/paper/CVLMU5PK"},"agent_actions":{"view_html":"https://pith.science/pith/CVLMU5PKXURSZT3U6KS5KMLITX","download_json":"https://pith.science/pith/CVLMU5PKXURSZT3U6KS5KMLITX.json","view_paper":"https://pith.science/paper/CVLMU5PK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.14462&json=true","fetch_graph":"https://pith.science/api/pith-number/CVLMU5PKXURSZT3U6KS5KMLITX/graph.json","fetch_events":"https://pith.science/api/pith-number/CVLMU5PKXURSZT3U6KS5KMLITX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CVLMU5PKXURSZT3U6KS5KMLITX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CVLMU5PKXURSZT3U6KS5KMLITX/action/storage_attestation","attest_author":"https://pith.science/pith/CVLMU5PKXURSZT3U6KS5KMLITX/action/author_attestation","sign_citation":"https://pith.science/pith/CVLMU5PKXURSZT3U6KS5KMLITX/action/citation_signature","submit_replication":"https://pith.science/pith/CVLMU5PKXURSZT3U6KS5KMLITX/action/replication_record"}},"created_at":"2026-07-05T07:37:56.647389+00:00","updated_at":"2026-07-05T07:37:56.647389+00:00"}