{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:T5NOLGYK244LHOP4OQBBAVMGWI","short_pith_number":"pith:T5NOLGYK","schema_version":"1.0","canonical_sha256":"9f5ae59b0ad738b3b9fc7402105586b2346efb706fab119c58f847704dcbc764","source":{"kind":"arxiv","id":"1909.03166","version":1},"attestation_state":"computed","paper":{"title":"Equalizing Recourse across Groups","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CY","stat.ML"],"primary_cat":"cs.LG","authors_text":"Chitradeep Dutta Roy, Pegah Nokhiz, Suresh Venkatasubramanian, Vivek Gupta","submitted_at":"2019-09-07T01:50:06Z","abstract_excerpt":"The rise in machine learning-assisted decision-making has led to concerns about the fairness of the decisions and techniques to mitigate problems of discrimination. If a negative decision is made about an individual (denying a loan, rejecting an application for housing, and so on) justice dictates that we be able to ask how we might change circumstances to get a favorable decision the next time. Moreover, the ability to change circumstances (a better education, improved credentials) should not be limited to only those with access to expensive resources. In other words, \\emph{recourse} for nega"},"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":"1909.03166","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-09-07T01:50:06Z","cross_cats_sorted":["cs.AI","cs.CY","stat.ML"],"title_canon_sha256":"ab190f1b25a9a72884d02e53cf9e9e2083c4ff97daa18258789e4330051d8c80","abstract_canon_sha256":"b73f115ab3b2cf15fd3c5ba41014f21ba6d3a26bf88ee91894448626e843146c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:03:03.154961Z","signature_b64":"TTAlSvm3EqorP4XLSsmnt5WRUtcGg8XkKO20VYnaq5ADfRllhWJTpPn+eoGyKF11A76U7gQnZTvExFvxBVLjBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9f5ae59b0ad738b3b9fc7402105586b2346efb706fab119c58f847704dcbc764","last_reissued_at":"2026-07-05T00:03:03.154482Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:03:03.154482Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Equalizing Recourse across Groups","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CY","stat.ML"],"primary_cat":"cs.LG","authors_text":"Chitradeep Dutta Roy, Pegah Nokhiz, Suresh Venkatasubramanian, Vivek Gupta","submitted_at":"2019-09-07T01:50:06Z","abstract_excerpt":"The rise in machine learning-assisted decision-making has led to concerns about the fairness of the decisions and techniques to mitigate problems of discrimination. If a negative decision is made about an individual (denying a loan, rejecting an application for housing, and so on) justice dictates that we be able to ask how we might change circumstances to get a favorable decision the next time. Moreover, the ability to change circumstances (a better education, improved credentials) should not be limited to only those with access to expensive resources. In other words, \\emph{recourse} for nega"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.03166","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/1909.03166/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":"1909.03166","created_at":"2026-07-05T00:03:03.154541+00:00"},{"alias_kind":"arxiv_version","alias_value":"1909.03166v1","created_at":"2026-07-05T00:03:03.154541+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.03166","created_at":"2026-07-05T00:03:03.154541+00:00"},{"alias_kind":"pith_short_12","alias_value":"T5NOLGYK244L","created_at":"2026-07-05T00:03:03.154541+00:00"},{"alias_kind":"pith_short_16","alias_value":"T5NOLGYK244LHOP4","created_at":"2026-07-05T00:03:03.154541+00:00"},{"alias_kind":"pith_short_8","alias_value":"T5NOLGYK","created_at":"2026-07-05T00:03:03.154541+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2410.01580","citing_title":"Learning-Augmented Robust Algorithmic Recourse","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12701","citing_title":"Do Fair Models Reason Fairly? Counterfactual Explanation Consistency for Procedural Fairness in Credit Decisions","ref_index":127,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09852","citing_title":"Fairness of Explanations in Artificial Intelligence (AI): A Unifying Framework, Axioms, and Future Direction toward Responsible AI","ref_index":23,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/T5NOLGYK244LHOP4OQBBAVMGWI","json":"https://pith.science/pith/T5NOLGYK244LHOP4OQBBAVMGWI.json","graph_json":"https://pith.science/api/pith-number/T5NOLGYK244LHOP4OQBBAVMGWI/graph.json","events_json":"https://pith.science/api/pith-number/T5NOLGYK244LHOP4OQBBAVMGWI/events.json","paper":"https://pith.science/paper/T5NOLGYK"},"agent_actions":{"view_html":"https://pith.science/pith/T5NOLGYK244LHOP4OQBBAVMGWI","download_json":"https://pith.science/pith/T5NOLGYK244LHOP4OQBBAVMGWI.json","view_paper":"https://pith.science/paper/T5NOLGYK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1909.03166&json=true","fetch_graph":"https://pith.science/api/pith-number/T5NOLGYK244LHOP4OQBBAVMGWI/graph.json","fetch_events":"https://pith.science/api/pith-number/T5NOLGYK244LHOP4OQBBAVMGWI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T5NOLGYK244LHOP4OQBBAVMGWI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T5NOLGYK244LHOP4OQBBAVMGWI/action/storage_attestation","attest_author":"https://pith.science/pith/T5NOLGYK244LHOP4OQBBAVMGWI/action/author_attestation","sign_citation":"https://pith.science/pith/T5NOLGYK244LHOP4OQBBAVMGWI/action/citation_signature","submit_replication":"https://pith.science/pith/T5NOLGYK244LHOP4OQBBAVMGWI/action/replication_record"}},"created_at":"2026-07-05T00:03:03.154541+00:00","updated_at":"2026-07-05T00:03:03.154541+00:00"}