{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:MBM5NCRQL5GJBN6YSCX3KDC3OF","short_pith_number":"pith:MBM5NCRQ","schema_version":"1.0","canonical_sha256":"6059d68a305f4c90b7d890afb50c5b7160113a0b23bb801cb64d3891c50edb80","source":{"kind":"arxiv","id":"2505.05211","version":1},"attestation_state":"computed","paper":{"title":"Incentive-Aware Machine Learning; Robustness, Fairness, Improvement & Causality","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.GT","authors_text":"Chara Podimata","submitted_at":"2025-05-08T13:04:32Z","abstract_excerpt":"The article explores the emerging domain of incentive-aware machine learning (ML), which focuses on algorithmic decision-making in contexts where individuals can strategically modify their inputs to influence outcomes. It categorizes the research into three perspectives: robustness, aiming to design models resilient to \"gaming\"; fairness, analyzing the societal impacts of such systems; and improvement/causality, recognizing situations where strategic actions lead to genuine personal or societal improvement. The paper introduces a unified framework encapsulating models for these perspectives, i"},"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":"2505.05211","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GT","submitted_at":"2025-05-08T13:04:32Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d52fd563700dd22f43e3e7758232d4d1e1f0630c596b071244828a498ae4e720","abstract_canon_sha256":"2c7e650a28dc943bed8fde742ac245292f55b033d39d82678e19d03545aebc93"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:00:20.855250Z","signature_b64":"ZJAhe0tJUa8fhE1gFEoHNSzKeycrAuBBqo1zR48ds987nvxX/bLWipUuhcUnTIC/l6mY4NqjavWayCMHp4cBDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6059d68a305f4c90b7d890afb50c5b7160113a0b23bb801cb64d3891c50edb80","last_reissued_at":"2026-07-05T11:00:20.854661Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:00:20.854661Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Incentive-Aware Machine Learning; Robustness, Fairness, Improvement & Causality","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.GT","authors_text":"Chara Podimata","submitted_at":"2025-05-08T13:04:32Z","abstract_excerpt":"The article explores the emerging domain of incentive-aware machine learning (ML), which focuses on algorithmic decision-making in contexts where individuals can strategically modify their inputs to influence outcomes. It categorizes the research into three perspectives: robustness, aiming to design models resilient to \"gaming\"; fairness, analyzing the societal impacts of such systems; and improvement/causality, recognizing situations where strategic actions lead to genuine personal or societal improvement. The paper introduces a unified framework encapsulating models for these perspectives, i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.05211","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/2505.05211/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":"2505.05211","created_at":"2026-07-05T11:00:20.854725+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.05211v1","created_at":"2026-07-05T11:00:20.854725+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.05211","created_at":"2026-07-05T11:00:20.854725+00:00"},{"alias_kind":"pith_short_12","alias_value":"MBM5NCRQL5GJ","created_at":"2026-07-05T11:00:20.854725+00:00"},{"alias_kind":"pith_short_16","alias_value":"MBM5NCRQL5GJBN6Y","created_at":"2026-07-05T11:00:20.854725+00:00"},{"alias_kind":"pith_short_8","alias_value":"MBM5NCRQ","created_at":"2026-07-05T11:00:20.854725+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.06378","citing_title":"Revisiting Fairness Impossibility with Endogenous Behavior","ref_index":9,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MBM5NCRQL5GJBN6YSCX3KDC3OF","json":"https://pith.science/pith/MBM5NCRQL5GJBN6YSCX3KDC3OF.json","graph_json":"https://pith.science/api/pith-number/MBM5NCRQL5GJBN6YSCX3KDC3OF/graph.json","events_json":"https://pith.science/api/pith-number/MBM5NCRQL5GJBN6YSCX3KDC3OF/events.json","paper":"https://pith.science/paper/MBM5NCRQ"},"agent_actions":{"view_html":"https://pith.science/pith/MBM5NCRQL5GJBN6YSCX3KDC3OF","download_json":"https://pith.science/pith/MBM5NCRQL5GJBN6YSCX3KDC3OF.json","view_paper":"https://pith.science/paper/MBM5NCRQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.05211&json=true","fetch_graph":"https://pith.science/api/pith-number/MBM5NCRQL5GJBN6YSCX3KDC3OF/graph.json","fetch_events":"https://pith.science/api/pith-number/MBM5NCRQL5GJBN6YSCX3KDC3OF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MBM5NCRQL5GJBN6YSCX3KDC3OF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MBM5NCRQL5GJBN6YSCX3KDC3OF/action/storage_attestation","attest_author":"https://pith.science/pith/MBM5NCRQL5GJBN6YSCX3KDC3OF/action/author_attestation","sign_citation":"https://pith.science/pith/MBM5NCRQL5GJBN6YSCX3KDC3OF/action/citation_signature","submit_replication":"https://pith.science/pith/MBM5NCRQL5GJBN6YSCX3KDC3OF/action/replication_record"}},"created_at":"2026-07-05T11:00:20.854725+00:00","updated_at":"2026-07-05T11:00:20.854725+00:00"}