{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:MQLTIA5MRKAB65RWUFF6U4N4AY","short_pith_number":"pith:MQLTIA5M","schema_version":"1.0","canonical_sha256":"64173403ac8a801f7636a14bea71bc063f72bac3b3a2b55875936795e00c621d","source":{"kind":"arxiv","id":"2608.05800","version":1},"attestation_state":"computed","paper":{"title":"Validity, Reliability, and Transparency in Artificial Intelligence Regulation","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CY","authors_text":"A. Mukundan, Debayan Gupta, Subhashis Banerjee","submitted_at":"2026-08-06T09:36:15Z","abstract_excerpt":"Artificial intelligence (AI) systems increasingly mediate decisions affecting individuals and societies. Existing data protection frameworks address certain privacy-related harms, particularly those arising from data leakage, re-identification, and profiling. However, they inadequately capture a more fundamental risk: unreliable or unjustified inference produced by AI systems even when data collection and processing are legitimate. This article argues that modern AI raises distinct concerns of construct validity, confounding, representativeness, distribution shift, and fairness trade-offs that"},"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":"2608.05800","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CY","submitted_at":"2026-08-06T09:36:15Z","cross_cats_sorted":[],"title_canon_sha256":"d6da117a3efb8913332787d060266bdd332a7a69f380d76c2c3e9dfc5e786c0e","abstract_canon_sha256":"9d23f2e7976beda0121d8ba3a315a7464eea4aacd2d308053b513feb0fa3217a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-07T00:52:46.700467Z","signature_b64":"dWoqH0fJaxTEiGGRxyjOuNcHhd1trmyHwCIf3LqKV4NlUdKw0VI7xivQPYaDbNxFHSYsekNOF7kO3DPGadLbDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"64173403ac8a801f7636a14bea71bc063f72bac3b3a2b55875936795e00c621d","last_reissued_at":"2026-08-07T00:52:46.698692Z","signature_status":"signed_v1","first_computed_at":"2026-08-07T00:52:46.698692Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Validity, Reliability, and Transparency in Artificial Intelligence Regulation","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CY","authors_text":"A. Mukundan, Debayan Gupta, Subhashis Banerjee","submitted_at":"2026-08-06T09:36:15Z","abstract_excerpt":"Artificial intelligence (AI) systems increasingly mediate decisions affecting individuals and societies. Existing data protection frameworks address certain privacy-related harms, particularly those arising from data leakage, re-identification, and profiling. However, they inadequately capture a more fundamental risk: unreliable or unjustified inference produced by AI systems even when data collection and processing are legitimate. This article argues that modern AI raises distinct concerns of construct validity, confounding, representativeness, distribution shift, and fairness trade-offs that"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.05800","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/2608.05800/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":"2608.05800","created_at":"2026-08-07T00:52:46.700322+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.05800v1","created_at":"2026-08-07T00:52:46.700322+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.05800","created_at":"2026-08-07T00:52:46.700322+00:00"},{"alias_kind":"pith_short_12","alias_value":"MQLTIA5MRKAB","created_at":"2026-08-07T00:52:46.700322+00:00"},{"alias_kind":"pith_short_16","alias_value":"MQLTIA5MRKAB65RW","created_at":"2026-08-07T00:52:46.700322+00:00"},{"alias_kind":"pith_short_8","alias_value":"MQLTIA5M","created_at":"2026-08-07T00:52:46.700322+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/MQLTIA5MRKAB65RWUFF6U4N4AY","json":"https://pith.science/pith/MQLTIA5MRKAB65RWUFF6U4N4AY.json","graph_json":"https://pith.science/api/pith-number/MQLTIA5MRKAB65RWUFF6U4N4AY/graph.json","events_json":"https://pith.science/api/pith-number/MQLTIA5MRKAB65RWUFF6U4N4AY/events.json","paper":"https://pith.science/paper/MQLTIA5M"},"agent_actions":{"view_html":"https://pith.science/pith/MQLTIA5MRKAB65RWUFF6U4N4AY","download_json":"https://pith.science/pith/MQLTIA5MRKAB65RWUFF6U4N4AY.json","view_paper":"https://pith.science/paper/MQLTIA5M","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.05800&json=true","fetch_graph":"https://pith.science/api/pith-number/MQLTIA5MRKAB65RWUFF6U4N4AY/graph.json","fetch_events":"https://pith.science/api/pith-number/MQLTIA5MRKAB65RWUFF6U4N4AY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MQLTIA5MRKAB65RWUFF6U4N4AY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MQLTIA5MRKAB65RWUFF6U4N4AY/action/storage_attestation","attest_author":"https://pith.science/pith/MQLTIA5MRKAB65RWUFF6U4N4AY/action/author_attestation","sign_citation":"https://pith.science/pith/MQLTIA5MRKAB65RWUFF6U4N4AY/action/citation_signature","submit_replication":"https://pith.science/pith/MQLTIA5MRKAB65RWUFF6U4N4AY/action/replication_record"}},"created_at":"2026-08-07T00:52:46.700322+00:00","updated_at":"2026-08-07T00:52:46.700322+00:00"}