{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:ZIFM76DFXLXTDHOYAZG4X3VJ3F","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e7ea75fd89b10444d2635ec8ae08ee598bfb7dc103cb52acbd3b4b12aef0dbff","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-15T00:52:46Z","title_canon_sha256":"c6687e5a7c3c28a1c336a51895f205900398dcb1dd3eb8c9b1b08fccebd51066"},"schema_version":"1.0","source":{"id":"2605.15504","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15504","created_at":"2026-05-20T00:01:02Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15504v1","created_at":"2026-05-20T00:01:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15504","created_at":"2026-05-20T00:01:02Z"},{"alias_kind":"pith_short_12","alias_value":"ZIFM76DFXLXT","created_at":"2026-05-20T00:01:02Z"},{"alias_kind":"pith_short_16","alias_value":"ZIFM76DFXLXTDHOY","created_at":"2026-05-20T00:01:02Z"},{"alias_kind":"pith_short_8","alias_value":"ZIFM76DF","created_at":"2026-05-20T00:01:02Z"}],"graph_snapshots":[{"event_id":"sha256:f625e56df29dcda57a4e24836b929495bc159ec662003997331835ba381c0175","target":"graph","created_at":"2026-05-20T00:01:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"We propose a game-theoretic framework that models the interaction between ML systems and users with conflicts of interest. We present scalable algorithms with theoretical guarantees that maximize the amount of desired information and actions and minimize the amount of biased and manipulative actions in interaction with ML systems."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That real-world conflicts of interest between ML owners and users can be accurately captured by a game-theoretic model and that scalable algorithms with theoretical guarantees can be developed to protect users without needing cooperation from the ML system owners."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A game-theoretic framework and algorithms are introduced to maximize beneficial information from ML systems while minimizing biased influences arising from conflicts of interest."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A game-theoretic framework enables users to extract useful information from ML systems while shielding themselves from biased and manipulative outputs even when system owners have conflicting goals."}],"snapshot_sha256":"e149ccd096c33baa110299cf54e3ee33e1a6e7868b8c894c9c37326314b44280"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"142791697f2e81bad6cedb9bae46ba49096586b4d9e548e9e60c7554b81ca44f"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T16:31:18.183908Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T16:04:38.792632Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"cited_work_retraction","ran_at":"2026-05-19T14:51:55.680604Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T14:21:54.061003Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"shingle_duplication","ran_at":"2026-05-19T13:49:41.852638Z","status":"skipped","version":"0.1.0"},{"findings_count":0,"name":"citation_quote_validity","ran_at":"2026-05-19T13:49:41.391517Z","status":"skipped","version":"0.1.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T13:33:22.639469Z","status":"skipped","version":"1.0.0"}],"endpoint":"/pith/2605.15504/integrity.json","findings":[],"snapshot_sha256":"5f062298a66b3c744fc837e04667e5a62728dcc59324bb6e07798ebe133dba1b","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Financial, social, and political factors often prevent the interests of the owners of ML systems and services and their users from being perfectly aligned. ML systems often produce biased information that can influence users to make decisions that are not in their best interest. Current solution approaches require ML systems to implement protocols to mitigate their biases. However, ML system owners usually do not have any incentive to implement these protocols and often argue that it limits their freedom of expression or business. We believe that a successful solution to this problem must reco","authors_text":"Ali Vakilian, Arash Termehchy, Marianne Winslett, Nischal Aryal","cross_cats":["cs.AI"],"headline":"A game-theoretic framework enables users to extract useful information from ML systems while shielding themselves from biased and manipulative outputs even when system owners have conflicting goals.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-15T00:52:46Z","title":"Learning with Conflicts of Interest"},"references":{"count":33,"internal_anchors":2,"resolved_work":33,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Information discrepancy in strategic learning","work_id":"f48549d1-aa2f-4f4b-937b-4cca685b269e","year":2022},{"cited_arxiv_id":"","doi":"10.24432/c5xw20","is_internal_anchor":false,"ref_index":2,"title":"UCI Machine Learning Repository (1996)","work_id":"87bf1dec-b317-4d1c-834f-647506c211f5","year":1996},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Poisoning attacks against support vector machines","work_id":"94f25b63-603e-4c4d-bf34-3e7253c40f0c","year":2012},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Persuade me if you can: A framework for evaluating persuasion effectiveness and susceptibility among large language models, 2025","work_id":"b55eddce-d344-4f18-9b72-efbff7774f9f","year":2025},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Bayesian strategic classification","work_id":"72530adf-0036-4a95-915c-9d330563e3e3","year":2024}],"snapshot_sha256":"9840d09ebee2652162d6e12740c330f2d79f29bb75d8bd2f25a5642f513b6dbd"},"source":{"id":"2605.15504","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T15:59:59.016753Z","id":"7c4a04f5-7ec2-4804-84d4-f55e8388416a","model_set":{"reader":"grok-4.3"},"one_line_summary":"A game-theoretic framework and algorithms are introduced to maximize beneficial information from ML systems while minimizing biased influences arising from conflicts of interest.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A game-theoretic framework enables users to extract useful information from ML systems while shielding themselves from biased and manipulative outputs even when system owners have conflicting goals.","strongest_claim":"We propose a game-theoretic framework that models the interaction between ML systems and users with conflicts of interest. We present scalable algorithms with theoretical guarantees that maximize the amount of desired information and actions and minimize the amount of biased and manipulative actions in interaction with ML systems.","weakest_assumption":"That real-world conflicts of interest between ML owners and users can be accurately captured by a game-theoretic model and that scalable algorithms with theoretical guarantees can be developed to protect users without needing cooperation from the ML system owners."}},"verdict_id":"7c4a04f5-7ec2-4804-84d4-f55e8388416a"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9ee0c612951f29e7a7313c61a619e93806c0a2d2d1e7cb3af36f18aa4bebc2c5","target":"record","created_at":"2026-05-20T00:01:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"e7ea75fd89b10444d2635ec8ae08ee598bfb7dc103cb52acbd3b4b12aef0dbff","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-15T00:52:46Z","title_canon_sha256":"c6687e5a7c3c28a1c336a51895f205900398dcb1dd3eb8c9b1b08fccebd51066"},"schema_version":"1.0","source":{"id":"2605.15504","kind":"arxiv","version":1}},"canonical_sha256":"ca0acff865baef319dd8064dcbeea9d9605e7728ddd277693201ed5b19e381bd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ca0acff865baef319dd8064dcbeea9d9605e7728ddd277693201ed5b19e381bd","first_computed_at":"2026-05-20T00:01:02.074320Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:01:02.074320Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FcD6G/B4d8BEyAzD1sW7r77/H8KIJ3HxPYlg+fD9k40lyU+/siwMJf+42tlZfFU85ALHnLM+4bo3HM6XFH2+Ag==","signature_status":"signed_v1","signed_at":"2026-05-20T00:01:02.075013Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.15504","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9ee0c612951f29e7a7313c61a619e93806c0a2d2d1e7cb3af36f18aa4bebc2c5","sha256:f625e56df29dcda57a4e24836b929495bc159ec662003997331835ba381c0175"],"state_sha256":"537712cf0ba7522fe5a81025d6fa6d2c3efb8c04f1fda461ad5d73b52caa1971"}