{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:DTCLFDCH6DYNTEFCWRLMXBXDG7","short_pith_number":"pith:DTCLFDCH","schema_version":"1.0","canonical_sha256":"1cc4b28c47f0f0d990a2b456cb86e337ddbb34d5773c196a89447eff7517c1c9","source":{"kind":"arxiv","id":"2506.11242","version":1},"attestation_state":"computed","paper":{"title":"A Causal Lens for Learning Long-term Fair Policies","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Jacob Lear, Lu Zhang","submitted_at":"2025-06-12T19:22:50Z","abstract_excerpt":"Fairness-aware learning studies the development of algorithms that avoid discriminatory decision outcomes despite biased training data. While most studies have concentrated on immediate bias in static contexts, this paper highlights the importance of investigating long-term fairness in dynamic decision-making systems while simultaneously considering instantaneous fairness requirements. In the context of reinforcement learning, we propose a general framework where long-term fairness is measured by the difference in the average expected qualification gain that individuals from different groups c"},"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":"2506.11242","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-12T19:22:50Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"2123de174db9fb967ace9b51d6caefd0c05c83026cf82b225492a896fc3bc0f2","abstract_canon_sha256":"4105d8e5d3e42b0456d30a22fb64e837db304987c9beea0cd1fa2d32b7e3cbe6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:21:01.594365Z","signature_b64":"krAVBRDcEp/fn3hFwpDtr3qVDEhhngF+3wx23kpAdiuxMAc+8qVLj6zyr8coLE91uy36rbjdwppQc14jhdWmDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1cc4b28c47f0f0d990a2b456cb86e337ddbb34d5773c196a89447eff7517c1c9","last_reissued_at":"2026-07-05T11:21:01.593934Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:21:01.593934Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Causal Lens for Learning Long-term Fair Policies","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Jacob Lear, Lu Zhang","submitted_at":"2025-06-12T19:22:50Z","abstract_excerpt":"Fairness-aware learning studies the development of algorithms that avoid discriminatory decision outcomes despite biased training data. While most studies have concentrated on immediate bias in static contexts, this paper highlights the importance of investigating long-term fairness in dynamic decision-making systems while simultaneously considering instantaneous fairness requirements. In the context of reinforcement learning, we propose a general framework where long-term fairness is measured by the difference in the average expected qualification gain that individuals from different groups c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.11242","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/2506.11242/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":"2506.11242","created_at":"2026-07-05T11:21:01.593989+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.11242v1","created_at":"2026-07-05T11:21:01.593989+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.11242","created_at":"2026-07-05T11:21:01.593989+00:00"},{"alias_kind":"pith_short_12","alias_value":"DTCLFDCH6DYN","created_at":"2026-07-05T11:21:01.593989+00:00"},{"alias_kind":"pith_short_16","alias_value":"DTCLFDCH6DYNTEFC","created_at":"2026-07-05T11:21:01.593989+00:00"},{"alias_kind":"pith_short_8","alias_value":"DTCLFDCH","created_at":"2026-07-05T11:21:01.593989+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/DTCLFDCH6DYNTEFCWRLMXBXDG7","json":"https://pith.science/pith/DTCLFDCH6DYNTEFCWRLMXBXDG7.json","graph_json":"https://pith.science/api/pith-number/DTCLFDCH6DYNTEFCWRLMXBXDG7/graph.json","events_json":"https://pith.science/api/pith-number/DTCLFDCH6DYNTEFCWRLMXBXDG7/events.json","paper":"https://pith.science/paper/DTCLFDCH"},"agent_actions":{"view_html":"https://pith.science/pith/DTCLFDCH6DYNTEFCWRLMXBXDG7","download_json":"https://pith.science/pith/DTCLFDCH6DYNTEFCWRLMXBXDG7.json","view_paper":"https://pith.science/paper/DTCLFDCH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.11242&json=true","fetch_graph":"https://pith.science/api/pith-number/DTCLFDCH6DYNTEFCWRLMXBXDG7/graph.json","fetch_events":"https://pith.science/api/pith-number/DTCLFDCH6DYNTEFCWRLMXBXDG7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DTCLFDCH6DYNTEFCWRLMXBXDG7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DTCLFDCH6DYNTEFCWRLMXBXDG7/action/storage_attestation","attest_author":"https://pith.science/pith/DTCLFDCH6DYNTEFCWRLMXBXDG7/action/author_attestation","sign_citation":"https://pith.science/pith/DTCLFDCH6DYNTEFCWRLMXBXDG7/action/citation_signature","submit_replication":"https://pith.science/pith/DTCLFDCH6DYNTEFCWRLMXBXDG7/action/replication_record"}},"created_at":"2026-07-05T11:21:01.593989+00:00","updated_at":"2026-07-05T11:21:01.593989+00:00"}