{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:X6W5Z6SDLDTIEHRMUTMRB3F2CO","short_pith_number":"pith:X6W5Z6SD","schema_version":"1.0","canonical_sha256":"bfaddcfa4358e6821e2ca4d910ecba13959e41a28ba946d6af50a4402e100a87","source":{"kind":"arxiv","id":"1912.05511","version":3},"attestation_state":"computed","paper":{"title":"Measurement and Fairness","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CY","authors_text":"Abigail Z. Jacobs, Hanna Wallach","submitted_at":"2019-12-11T18:21:38Z","abstract_excerpt":"We propose measurement modeling from the quantitative social sciences as a framework for understanding fairness in computational systems. Computational systems often involve unobservable theoretical constructs, such as socioeconomic status, teacher effectiveness, and risk of recidivism. Such constructs cannot be measured directly and must instead be inferred from measurements of observable properties (and other unobservable theoretical constructs) thought to be related to them -- i.e., operationalized via a measurement model. This process, which necessarily involves making assumptions, introdu"},"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":"1912.05511","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2019-12-11T18:21:38Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"7d4b26045eaf907046a943c9d199bd908dcafa8f9094002b4d9fa16d43a2a681","abstract_canon_sha256":"b84f0167010672a44f32b0a7b98212ea4eed3194a0f1e698afaa1d7aaf172273"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:22:34.748240Z","signature_b64":"Zz7ujrlwey7p5Gt57lu2roX1Z8qY/WhT6NsJq2K4jW9ieKKufI1DTmgYG1rUuLWDiNFe9uT72g45HUsqd9bvBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bfaddcfa4358e6821e2ca4d910ecba13959e41a28ba946d6af50a4402e100a87","last_reissued_at":"2026-07-05T02:22:34.747713Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:22:34.747713Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Measurement and Fairness","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CY","authors_text":"Abigail Z. Jacobs, Hanna Wallach","submitted_at":"2019-12-11T18:21:38Z","abstract_excerpt":"We propose measurement modeling from the quantitative social sciences as a framework for understanding fairness in computational systems. Computational systems often involve unobservable theoretical constructs, such as socioeconomic status, teacher effectiveness, and risk of recidivism. Such constructs cannot be measured directly and must instead be inferred from measurements of observable properties (and other unobservable theoretical constructs) thought to be related to them -- i.e., operationalized via a measurement model. This process, which necessarily involves making assumptions, introdu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.05511","kind":"arxiv","version":3},"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/1912.05511/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":"1912.05511","created_at":"2026-07-05T02:22:34.747775+00:00"},{"alias_kind":"arxiv_version","alias_value":"1912.05511v3","created_at":"2026-07-05T02:22:34.747775+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.05511","created_at":"2026-07-05T02:22:34.747775+00:00"},{"alias_kind":"pith_short_12","alias_value":"X6W5Z6SDLDTI","created_at":"2026-07-05T02:22:34.747775+00:00"},{"alias_kind":"pith_short_16","alias_value":"X6W5Z6SDLDTIEHRM","created_at":"2026-07-05T02:22:34.747775+00:00"},{"alias_kind":"pith_short_8","alias_value":"X6W5Z6SD","created_at":"2026-07-05T02:22:34.747775+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.08044","citing_title":"When Behavioral Safety Evaluation Fails: A Representation-Level Perspective","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2603.06811","citing_title":"Making AI Evaluation Deployment Relevant Through Context Specification","ref_index":12,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/X6W5Z6SDLDTIEHRMUTMRB3F2CO","json":"https://pith.science/pith/X6W5Z6SDLDTIEHRMUTMRB3F2CO.json","graph_json":"https://pith.science/api/pith-number/X6W5Z6SDLDTIEHRMUTMRB3F2CO/graph.json","events_json":"https://pith.science/api/pith-number/X6W5Z6SDLDTIEHRMUTMRB3F2CO/events.json","paper":"https://pith.science/paper/X6W5Z6SD"},"agent_actions":{"view_html":"https://pith.science/pith/X6W5Z6SDLDTIEHRMUTMRB3F2CO","download_json":"https://pith.science/pith/X6W5Z6SDLDTIEHRMUTMRB3F2CO.json","view_paper":"https://pith.science/paper/X6W5Z6SD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1912.05511&json=true","fetch_graph":"https://pith.science/api/pith-number/X6W5Z6SDLDTIEHRMUTMRB3F2CO/graph.json","fetch_events":"https://pith.science/api/pith-number/X6W5Z6SDLDTIEHRMUTMRB3F2CO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/X6W5Z6SDLDTIEHRMUTMRB3F2CO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/X6W5Z6SDLDTIEHRMUTMRB3F2CO/action/storage_attestation","attest_author":"https://pith.science/pith/X6W5Z6SDLDTIEHRMUTMRB3F2CO/action/author_attestation","sign_citation":"https://pith.science/pith/X6W5Z6SDLDTIEHRMUTMRB3F2CO/action/citation_signature","submit_replication":"https://pith.science/pith/X6W5Z6SDLDTIEHRMUTMRB3F2CO/action/replication_record"}},"created_at":"2026-07-05T02:22:34.747775+00:00","updated_at":"2026-07-05T02:22:34.747775+00:00"}