{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:TZLXELYFIZGCCFOKJPTF6GKOV4","short_pith_number":"pith:TZLXELYF","schema_version":"1.0","canonical_sha256":"9e57722f05464c2115ca4be65f194eaf2d442888b6744f7d178cc2a9dfe59c45","source":{"kind":"arxiv","id":"1912.06883","version":1},"attestation_state":"computed","paper":{"title":"On the Apparent Conflict Between Individual and Group Fairness","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CY","stat.ML"],"primary_cat":"cs.LG","authors_text":"Reuben Binns","submitted_at":"2019-12-14T17:13:15Z","abstract_excerpt":"A distinction has been drawn in fair machine learning research between `group' and `individual' fairness measures. Many technical research papers assume that both are important, but conflicting, and propose ways to minimise the trade-offs between these measures. This paper argues that this apparent conflict is based on a misconception. It draws on theoretical discussions from within the fair machine learning research, and from political and legal philosophy, to argue that individual and group fairness are not fundamentally in conflict. First, it outlines accounts of egalitarian fairness which "},"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":"1912.06883","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-12-14T17:13:15Z","cross_cats_sorted":["cs.CY","stat.ML"],"title_canon_sha256":"9f87d89cb66b07188e40f2046fa1c2e0936926a70eb381f343eda57add219701","abstract_canon_sha256":"30e0ff7d81130ac656a9240d201b98adcb50eddd3f41c717c3d2d2f662d55756"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:26:16.374189Z","signature_b64":"J4K+c6EFNhUgaLLLGuYDUwu6aV7wqKTyaWVNV5BquEBJwS4HL497bbTIZ82qtdnBkLHGHaUrhKg3UKx+JMk+DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9e57722f05464c2115ca4be65f194eaf2d442888b6744f7d178cc2a9dfe59c45","last_reissued_at":"2026-07-05T00:26:16.373780Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:26:16.373780Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On the Apparent Conflict Between Individual and Group Fairness","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CY","stat.ML"],"primary_cat":"cs.LG","authors_text":"Reuben Binns","submitted_at":"2019-12-14T17:13:15Z","abstract_excerpt":"A distinction has been drawn in fair machine learning research between `group' and `individual' fairness measures. Many technical research papers assume that both are important, but conflicting, and propose ways to minimise the trade-offs between these measures. This paper argues that this apparent conflict is based on a misconception. It draws on theoretical discussions from within the fair machine learning research, and from political and legal philosophy, to argue that individual and group fairness are not fundamentally in conflict. First, it outlines accounts of egalitarian fairness which "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.06883","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/1912.06883/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":"1912.06883","created_at":"2026-07-05T00:26:16.373830+00:00"},{"alias_kind":"arxiv_version","alias_value":"1912.06883v1","created_at":"2026-07-05T00:26:16.373830+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.06883","created_at":"2026-07-05T00:26:16.373830+00:00"},{"alias_kind":"pith_short_12","alias_value":"TZLXELYFIZGC","created_at":"2026-07-05T00:26:16.373830+00:00"},{"alias_kind":"pith_short_16","alias_value":"TZLXELYFIZGCCFOK","created_at":"2026-07-05T00:26:16.373830+00:00"},{"alias_kind":"pith_short_8","alias_value":"TZLXELYF","created_at":"2026-07-05T00:26:16.373830+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2412.16641","citing_title":"A Systems Thinking Approach to Algorithmic Fairness","ref_index":6,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TZLXELYFIZGCCFOKJPTF6GKOV4","json":"https://pith.science/pith/TZLXELYFIZGCCFOKJPTF6GKOV4.json","graph_json":"https://pith.science/api/pith-number/TZLXELYFIZGCCFOKJPTF6GKOV4/graph.json","events_json":"https://pith.science/api/pith-number/TZLXELYFIZGCCFOKJPTF6GKOV4/events.json","paper":"https://pith.science/paper/TZLXELYF"},"agent_actions":{"view_html":"https://pith.science/pith/TZLXELYFIZGCCFOKJPTF6GKOV4","download_json":"https://pith.science/pith/TZLXELYFIZGCCFOKJPTF6GKOV4.json","view_paper":"https://pith.science/paper/TZLXELYF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1912.06883&json=true","fetch_graph":"https://pith.science/api/pith-number/TZLXELYFIZGCCFOKJPTF6GKOV4/graph.json","fetch_events":"https://pith.science/api/pith-number/TZLXELYFIZGCCFOKJPTF6GKOV4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TZLXELYFIZGCCFOKJPTF6GKOV4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TZLXELYFIZGCCFOKJPTF6GKOV4/action/storage_attestation","attest_author":"https://pith.science/pith/TZLXELYFIZGCCFOKJPTF6GKOV4/action/author_attestation","sign_citation":"https://pith.science/pith/TZLXELYFIZGCCFOKJPTF6GKOV4/action/citation_signature","submit_replication":"https://pith.science/pith/TZLXELYFIZGCCFOKJPTF6GKOV4/action/replication_record"}},"created_at":"2026-07-05T00:26:16.373830+00:00","updated_at":"2026-07-05T00:26:16.373830+00:00"}