{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:K6VLKBK5TEOABTOKBFKYOIGMG7","short_pith_number":"pith:K6VLKBK5","schema_version":"1.0","canonical_sha256":"57aab5055d991c00cdca09558720cc37cd718c0143f85af4369c768faa1b8b10","source":{"kind":"arxiv","id":"2103.06172","version":2},"attestation_state":"computed","paper":{"title":"Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.LG","authors_text":"Bobbie Chern, Chlo\\'e Bakalar, Edmund Tong, Isabel Kloumann, Jiejing Zhao, Joaquin Qui\\~nonero Candela, Jonathan Tannen, Joshua Simons, Kate Vredenburgh, Manish Raghavan, Melissa Hall, Michelle Lam, Miranda Bogen, Renata Barreto, Sam Corbett-Davies, Stevie Bergman","submitted_at":"2021-03-10T16:42:20Z","abstract_excerpt":"Many technical approaches have been proposed for ensuring that decisions made by machine learning systems are fair, but few of these proposals have been stress-tested in real-world systems. This paper presents an example of one team's approach to the challenge of applying algorithmic fairness approaches to complex production systems within the context of a large technology company. We discuss how we disentangle normative questions of product and policy design (like, \"how should the system trade off between different stakeholders' interests and needs?\") from empirical questions of system implem"},"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":"2103.06172","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-03-10T16:42:20Z","cross_cats_sorted":["cs.CY"],"title_canon_sha256":"8940c1a63226df80066747212ec2ceb96f519d5bb4deb358468469956022cc83","abstract_canon_sha256":"bcc75350328480510dd84557427aac4c370f048654548334bd1d687f1b19e536"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:26:03.470416Z","signature_b64":"dKBZLrOYpTShZGjgXFa9hflfzQYNnmGE7K2myffwDLpFaWhkgyVa6jWvKIXIUS27gb4iw08oiRYiXGd1Py4ZCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"57aab5055d991c00cdca09558720cc37cd718c0143f85af4369c768faa1b8b10","last_reissued_at":"2026-07-05T02:26:03.470005Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:26:03.470005Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.LG","authors_text":"Bobbie Chern, Chlo\\'e Bakalar, Edmund Tong, Isabel Kloumann, Jiejing Zhao, Joaquin Qui\\~nonero Candela, Jonathan Tannen, Joshua Simons, Kate Vredenburgh, Manish Raghavan, Melissa Hall, Michelle Lam, Miranda Bogen, Renata Barreto, Sam Corbett-Davies, Stevie Bergman","submitted_at":"2021-03-10T16:42:20Z","abstract_excerpt":"Many technical approaches have been proposed for ensuring that decisions made by machine learning systems are fair, but few of these proposals have been stress-tested in real-world systems. This paper presents an example of one team's approach to the challenge of applying algorithmic fairness approaches to complex production systems within the context of a large technology company. We discuss how we disentangle normative questions of product and policy design (like, \"how should the system trade off between different stakeholders' interests and needs?\") from empirical questions of system implem"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.06172","kind":"arxiv","version":2},"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/2103.06172/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":"2103.06172","created_at":"2026-07-05T02:26:03.470064+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.06172v2","created_at":"2026-07-05T02:26:03.470064+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.06172","created_at":"2026-07-05T02:26:03.470064+00:00"},{"alias_kind":"pith_short_12","alias_value":"K6VLKBK5TEOA","created_at":"2026-07-05T02:26:03.470064+00:00"},{"alias_kind":"pith_short_16","alias_value":"K6VLKBK5TEOABTOK","created_at":"2026-07-05T02:26:03.470064+00:00"},{"alias_kind":"pith_short_8","alias_value":"K6VLKBK5","created_at":"2026-07-05T02:26:03.470064+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/K6VLKBK5TEOABTOKBFKYOIGMG7","json":"https://pith.science/pith/K6VLKBK5TEOABTOKBFKYOIGMG7.json","graph_json":"https://pith.science/api/pith-number/K6VLKBK5TEOABTOKBFKYOIGMG7/graph.json","events_json":"https://pith.science/api/pith-number/K6VLKBK5TEOABTOKBFKYOIGMG7/events.json","paper":"https://pith.science/paper/K6VLKBK5"},"agent_actions":{"view_html":"https://pith.science/pith/K6VLKBK5TEOABTOKBFKYOIGMG7","download_json":"https://pith.science/pith/K6VLKBK5TEOABTOKBFKYOIGMG7.json","view_paper":"https://pith.science/paper/K6VLKBK5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.06172&json=true","fetch_graph":"https://pith.science/api/pith-number/K6VLKBK5TEOABTOKBFKYOIGMG7/graph.json","fetch_events":"https://pith.science/api/pith-number/K6VLKBK5TEOABTOKBFKYOIGMG7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K6VLKBK5TEOABTOKBFKYOIGMG7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K6VLKBK5TEOABTOKBFKYOIGMG7/action/storage_attestation","attest_author":"https://pith.science/pith/K6VLKBK5TEOABTOKBFKYOIGMG7/action/author_attestation","sign_citation":"https://pith.science/pith/K6VLKBK5TEOABTOKBFKYOIGMG7/action/citation_signature","submit_replication":"https://pith.science/pith/K6VLKBK5TEOABTOKBFKYOIGMG7/action/replication_record"}},"created_at":"2026-07-05T02:26:03.470064+00:00","updated_at":"2026-07-05T02:26:03.470064+00:00"}