{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:TW43Q2LVBNAMVWXNJ67Q2XHZFF","short_pith_number":"pith:TW43Q2LV","schema_version":"1.0","canonical_sha256":"9db9b869750b40cadaed4fbf0d5cf929403b76f6edbd773f55bd76462c0c31b6","source":{"kind":"arxiv","id":"2510.19098","version":2},"attestation_state":"computed","paper":{"title":"Desirable Effort Fairness and Optimality Trade-offs in Strategic Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.GT","authors_text":"Chara Podimata, Ekaterina Fedorova, Valia Efthymiou","submitted_at":"2025-10-21T21:43:20Z","abstract_excerpt":"Strategic classification examines how decision rules interact with agents who strategically adapt their features. Most existing models focus on maximizing predictive performance, assuming agents best respond to the learned classifier. However, real decision-making systems are rarely optimized solely for accuracy: ethical, economic, and institutional considerations often make some feature changes more desirable than others. At the same time, principals may wish to incentivize these changes fairly across heterogeneous agents. While prior work has studied causal structure between features, notion"},"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":"2510.19098","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.GT","submitted_at":"2025-10-21T21:43:20Z","cross_cats_sorted":["cs.CY"],"title_canon_sha256":"75bf96ba70660cd9e29d2d9b55533bbcf0f2ec2b34b8acce1bd7de7483430f44","abstract_canon_sha256":"3e69fdb58679a774085436f5fb6c82ca69ecf466b6c9ec28d3d258646caa3266"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T00:15:50.434091Z","signature_b64":"s+jTBFYOzE7mvEGot9Gbn0jx6pEDKRhrfe2JMEyaHcO2WzoLKejsZRgj4+LfBj8E3B4YruaFicMX448PwMytDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9db9b869750b40cadaed4fbf0d5cf929403b76f6edbd773f55bd76462c0c31b6","last_reissued_at":"2026-07-07T00:15:50.433229Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T00:15:50.433229Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Desirable Effort Fairness and Optimality Trade-offs in Strategic Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.GT","authors_text":"Chara Podimata, Ekaterina Fedorova, Valia Efthymiou","submitted_at":"2025-10-21T21:43:20Z","abstract_excerpt":"Strategic classification examines how decision rules interact with agents who strategically adapt their features. Most existing models focus on maximizing predictive performance, assuming agents best respond to the learned classifier. However, real decision-making systems are rarely optimized solely for accuracy: ethical, economic, and institutional considerations often make some feature changes more desirable than others. At the same time, principals may wish to incentivize these changes fairly across heterogeneous agents. While prior work has studied causal structure between features, notion"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.19098","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/2510.19098/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":"2510.19098","created_at":"2026-07-07T00:15:50.433354+00:00"},{"alias_kind":"arxiv_version","alias_value":"2510.19098v2","created_at":"2026-07-07T00:15:50.433354+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.19098","created_at":"2026-07-07T00:15:50.433354+00:00"},{"alias_kind":"pith_short_12","alias_value":"TW43Q2LVBNAM","created_at":"2026-07-07T00:15:50.433354+00:00"},{"alias_kind":"pith_short_16","alias_value":"TW43Q2LVBNAMVWXN","created_at":"2026-07-07T00:15:50.433354+00:00"},{"alias_kind":"pith_short_8","alias_value":"TW43Q2LV","created_at":"2026-07-07T00:15:50.433354+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2606.01198","citing_title":"Linear Strategic Classification with Endogenous Improvements","ref_index":15,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TW43Q2LVBNAMVWXNJ67Q2XHZFF","json":"https://pith.science/pith/TW43Q2LVBNAMVWXNJ67Q2XHZFF.json","graph_json":"https://pith.science/api/pith-number/TW43Q2LVBNAMVWXNJ67Q2XHZFF/graph.json","events_json":"https://pith.science/api/pith-number/TW43Q2LVBNAMVWXNJ67Q2XHZFF/events.json","paper":"https://pith.science/paper/TW43Q2LV"},"agent_actions":{"view_html":"https://pith.science/pith/TW43Q2LVBNAMVWXNJ67Q2XHZFF","download_json":"https://pith.science/pith/TW43Q2LVBNAMVWXNJ67Q2XHZFF.json","view_paper":"https://pith.science/paper/TW43Q2LV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2510.19098&json=true","fetch_graph":"https://pith.science/api/pith-number/TW43Q2LVBNAMVWXNJ67Q2XHZFF/graph.json","fetch_events":"https://pith.science/api/pith-number/TW43Q2LVBNAMVWXNJ67Q2XHZFF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TW43Q2LVBNAMVWXNJ67Q2XHZFF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TW43Q2LVBNAMVWXNJ67Q2XHZFF/action/storage_attestation","attest_author":"https://pith.science/pith/TW43Q2LVBNAMVWXNJ67Q2XHZFF/action/author_attestation","sign_citation":"https://pith.science/pith/TW43Q2LVBNAMVWXNJ67Q2XHZFF/action/citation_signature","submit_replication":"https://pith.science/pith/TW43Q2LVBNAMVWXNJ67Q2XHZFF/action/replication_record"}},"created_at":"2026-07-07T00:15:50.433354+00:00","updated_at":"2026-07-07T00:15:50.433354+00:00"}