{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:VJOUDYQBPGV3VPUMABSUBOUCYU","short_pith_number":"pith:VJOUDYQB","schema_version":"1.0","canonical_sha256":"aa5d41e20179abbabe8c006540ba82c50b7010882aeea102202c5d57c305d6fc","source":{"kind":"arxiv","id":"2504.17544","version":1},"attestation_state":"computed","paper":{"title":"Auditing the Ethical Logic of Generative AI Models","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Ali Dasdan, Chad Coleman, Manan Shah, Safinah Ali, W. Russell Neuman","submitted_at":"2025-04-24T13:32:30Z","abstract_excerpt":"As generative AI models become increasingly integrated into high-stakes domains, the need for robust methods to evaluate their ethical reasoning becomes increasingly important. This paper introduces a five-dimensional audit model -- assessing Analytic Quality, Breadth of Ethical Considerations, Depth of Explanation, Consistency, and Decisiveness -- to evaluate the ethical logic of leading large language models (LLMs). Drawing on traditions from applied ethics and higher-order thinking, we present a multi-battery prompt approach, including novel ethical dilemmas, to probe the models' reasoning "},"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":"2504.17544","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2025-04-24T13:32:30Z","cross_cats_sorted":[],"title_canon_sha256":"0f58bc4cbe48b94cef5bba53dae0bb0ecf50efd35e6b49545cb62644d39340e6","abstract_canon_sha256":"3aa3cac69fe6099a23ff16993272e3dbe0543bbde0e7814f170b899dddb6d8fe"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:53:30.666184Z","signature_b64":"BIyE7F+YP+/dafPpuvfw72JTMdjjRf/ZplkiUyyksDMpI89RhMplo1Pmo4k8+1FNxailk7A1DycoHTki7k8PDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aa5d41e20179abbabe8c006540ba82c50b7010882aeea102202c5d57c305d6fc","last_reissued_at":"2026-07-05T10:53:30.665605Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:53:30.665605Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Auditing the Ethical Logic of Generative AI Models","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Ali Dasdan, Chad Coleman, Manan Shah, Safinah Ali, W. Russell Neuman","submitted_at":"2025-04-24T13:32:30Z","abstract_excerpt":"As generative AI models become increasingly integrated into high-stakes domains, the need for robust methods to evaluate their ethical reasoning becomes increasingly important. This paper introduces a five-dimensional audit model -- assessing Analytic Quality, Breadth of Ethical Considerations, Depth of Explanation, Consistency, and Decisiveness -- to evaluate the ethical logic of leading large language models (LLMs). Drawing on traditions from applied ethics and higher-order thinking, we present a multi-battery prompt approach, including novel ethical dilemmas, to probe the models' reasoning "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.17544","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/2504.17544/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":"2504.17544","created_at":"2026-07-05T10:53:30.665680+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.17544v1","created_at":"2026-07-05T10:53:30.665680+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.17544","created_at":"2026-07-05T10:53:30.665680+00:00"},{"alias_kind":"pith_short_12","alias_value":"VJOUDYQBPGV3","created_at":"2026-07-05T10:53:30.665680+00:00"},{"alias_kind":"pith_short_16","alias_value":"VJOUDYQBPGV3VPUM","created_at":"2026-07-05T10:53:30.665680+00:00"},{"alias_kind":"pith_short_8","alias_value":"VJOUDYQB","created_at":"2026-07-05T10:53:30.665680+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/VJOUDYQBPGV3VPUMABSUBOUCYU","json":"https://pith.science/pith/VJOUDYQBPGV3VPUMABSUBOUCYU.json","graph_json":"https://pith.science/api/pith-number/VJOUDYQBPGV3VPUMABSUBOUCYU/graph.json","events_json":"https://pith.science/api/pith-number/VJOUDYQBPGV3VPUMABSUBOUCYU/events.json","paper":"https://pith.science/paper/VJOUDYQB"},"agent_actions":{"view_html":"https://pith.science/pith/VJOUDYQBPGV3VPUMABSUBOUCYU","download_json":"https://pith.science/pith/VJOUDYQBPGV3VPUMABSUBOUCYU.json","view_paper":"https://pith.science/paper/VJOUDYQB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.17544&json=true","fetch_graph":"https://pith.science/api/pith-number/VJOUDYQBPGV3VPUMABSUBOUCYU/graph.json","fetch_events":"https://pith.science/api/pith-number/VJOUDYQBPGV3VPUMABSUBOUCYU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VJOUDYQBPGV3VPUMABSUBOUCYU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VJOUDYQBPGV3VPUMABSUBOUCYU/action/storage_attestation","attest_author":"https://pith.science/pith/VJOUDYQBPGV3VPUMABSUBOUCYU/action/author_attestation","sign_citation":"https://pith.science/pith/VJOUDYQBPGV3VPUMABSUBOUCYU/action/citation_signature","submit_replication":"https://pith.science/pith/VJOUDYQBPGV3VPUMABSUBOUCYU/action/replication_record"}},"created_at":"2026-07-05T10:53:30.665680+00:00","updated_at":"2026-07-05T10:53:30.665680+00:00"}