{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:H6VRCSPER7AW4JW3YEALHCUE6Z","short_pith_number":"pith:H6VRCSPE","schema_version":"1.0","canonical_sha256":"3fab1149e48fc16e26dbc100b38a84f643f9e330a7ce9e29fbf6ab028e9e7c3c","source":{"kind":"arxiv","id":"1912.04930","version":1},"attestation_state":"computed","paper":{"title":"The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasons","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CY","authors_text":"Andrew D. Selbst, Manish Raghavan, Solon Barocas","submitted_at":"2019-12-10T19:09:14Z","abstract_excerpt":"Counterfactual explanations are gaining prominence within technical, legal, and business circles as a way to explain the decisions of a machine learning model. These explanations share a trait with the long-established \"principal reason\" explanations required by U.S. credit laws: they both explain a decision by highlighting a set of features deemed most relevant--and withholding others.\n  These \"feature-highlighting explanations\" have several desirable properties: They place no constraints on model complexity, do not require model disclosure, detail what needed to be different to achieve a dif"},"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":"1912.04930","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2019-12-10T19:09:14Z","cross_cats_sorted":[],"title_canon_sha256":"e53bcb3988f28c37b3956199892dd4d07f4812162916507fbd2eee0eb8c10758","abstract_canon_sha256":"bba2f29cbebecf694ae9ebee0e37582a18c0c434c2babcfbf954cd657b8ce183"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:25:30.323836Z","signature_b64":"SjU+ixvcAFXWc2fx6CceTntLwAkWPq8cMVBmVwnvU+o81EfckFWUqctl3r8Yofb1gfSCCgHk7igCye1o7AENAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3fab1149e48fc16e26dbc100b38a84f643f9e330a7ce9e29fbf6ab028e9e7c3c","last_reissued_at":"2026-07-05T00:25:30.323341Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:25:30.323341Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasons","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CY","authors_text":"Andrew D. Selbst, Manish Raghavan, Solon Barocas","submitted_at":"2019-12-10T19:09:14Z","abstract_excerpt":"Counterfactual explanations are gaining prominence within technical, legal, and business circles as a way to explain the decisions of a machine learning model. These explanations share a trait with the long-established \"principal reason\" explanations required by U.S. credit laws: they both explain a decision by highlighting a set of features deemed most relevant--and withholding others.\n  These \"feature-highlighting explanations\" have several desirable properties: They place no constraints on model complexity, do not require model disclosure, detail what needed to be different to achieve a dif"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.04930","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/1912.04930/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":"1912.04930","created_at":"2026-07-05T00:25:30.323404+00:00"},{"alias_kind":"arxiv_version","alias_value":"1912.04930v1","created_at":"2026-07-05T00:25:30.323404+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.04930","created_at":"2026-07-05T00:25:30.323404+00:00"},{"alias_kind":"pith_short_12","alias_value":"H6VRCSPER7AW","created_at":"2026-07-05T00:25:30.323404+00:00"},{"alias_kind":"pith_short_16","alias_value":"H6VRCSPER7AW4JW3","created_at":"2026-07-05T00:25:30.323404+00:00"},{"alias_kind":"pith_short_8","alias_value":"H6VRCSPE","created_at":"2026-07-05T00:25:30.323404+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/H6VRCSPER7AW4JW3YEALHCUE6Z","json":"https://pith.science/pith/H6VRCSPER7AW4JW3YEALHCUE6Z.json","graph_json":"https://pith.science/api/pith-number/H6VRCSPER7AW4JW3YEALHCUE6Z/graph.json","events_json":"https://pith.science/api/pith-number/H6VRCSPER7AW4JW3YEALHCUE6Z/events.json","paper":"https://pith.science/paper/H6VRCSPE"},"agent_actions":{"view_html":"https://pith.science/pith/H6VRCSPER7AW4JW3YEALHCUE6Z","download_json":"https://pith.science/pith/H6VRCSPER7AW4JW3YEALHCUE6Z.json","view_paper":"https://pith.science/paper/H6VRCSPE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1912.04930&json=true","fetch_graph":"https://pith.science/api/pith-number/H6VRCSPER7AW4JW3YEALHCUE6Z/graph.json","fetch_events":"https://pith.science/api/pith-number/H6VRCSPER7AW4JW3YEALHCUE6Z/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H6VRCSPER7AW4JW3YEALHCUE6Z/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H6VRCSPER7AW4JW3YEALHCUE6Z/action/storage_attestation","attest_author":"https://pith.science/pith/H6VRCSPER7AW4JW3YEALHCUE6Z/action/author_attestation","sign_citation":"https://pith.science/pith/H6VRCSPER7AW4JW3YEALHCUE6Z/action/citation_signature","submit_replication":"https://pith.science/pith/H6VRCSPER7AW4JW3YEALHCUE6Z/action/replication_record"}},"created_at":"2026-07-05T00:25:30.323404+00:00","updated_at":"2026-07-05T00:25:30.323404+00:00"}