{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:VTA4YTNGEASRE7RY3UAIJ3B5UX","short_pith_number":"pith:VTA4YTNG","schema_version":"1.0","canonical_sha256":"acc1cc4da62025127e38dd0084ec3da5ec20da09d62ce52173eaea2596683e65","source":{"kind":"arxiv","id":"2302.04262","version":3},"attestation_state":"computed","paper":{"title":"Algorithmic Collective Action in Machine Learning","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.GT","stat.ML"],"primary_cat":"cs.LG","authors_text":"Celestine Mendler-D\\\"unner, Eric Mazumdar, Moritz Hardt, Tijana Zrnic","submitted_at":"2023-02-08T18:55:49Z","abstract_excerpt":"We initiate a principled study of algorithmic collective action on digital platforms that deploy machine learning algorithms. We propose a simple theoretical model of a collective interacting with a firm's learning algorithm. The collective pools the data of participating individuals and executes an algorithmic strategy by instructing participants how to modify their own data to achieve a collective goal. We investigate the consequences of this model in three fundamental learning-theoretic settings: the case of a nonparametric optimal learning algorithm, a parametric risk minimizer, and gradie"},"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":"2302.04262","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2023-02-08T18:55:49Z","cross_cats_sorted":["cs.GT","stat.ML"],"title_canon_sha256":"37f2afba6a3966302622658cb5eb8f9e9327c427dd85a4c92bb0f3c6a66f3be4","abstract_canon_sha256":"568d729d9fd08072594d2a98fd7530832abaf9ee1157d3f992d348da514262d4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:53:07.259144Z","signature_b64":"GuXSRUw9iedcLS5NVUyfY8zyASd48Vo7ufryFvkqvXBonB4FNZC3YnMxgsjsSPo69DEGXchOjXnVrk9o21l6BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"acc1cc4da62025127e38dd0084ec3da5ec20da09d62ce52173eaea2596683e65","last_reissued_at":"2026-07-05T08:53:07.258625Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:53:07.258625Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Algorithmic Collective Action in Machine Learning","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.GT","stat.ML"],"primary_cat":"cs.LG","authors_text":"Celestine Mendler-D\\\"unner, Eric Mazumdar, Moritz Hardt, Tijana Zrnic","submitted_at":"2023-02-08T18:55:49Z","abstract_excerpt":"We initiate a principled study of algorithmic collective action on digital platforms that deploy machine learning algorithms. We propose a simple theoretical model of a collective interacting with a firm's learning algorithm. The collective pools the data of participating individuals and executes an algorithmic strategy by instructing participants how to modify their own data to achieve a collective goal. We investigate the consequences of this model in three fundamental learning-theoretic settings: the case of a nonparametric optimal learning algorithm, a parametric risk minimizer, and gradie"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.04262","kind":"arxiv","version":3},"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/2302.04262/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":"2302.04262","created_at":"2026-07-05T08:53:07.258682+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.04262v3","created_at":"2026-07-05T08:53:07.258682+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.04262","created_at":"2026-07-05T08:53:07.258682+00:00"},{"alias_kind":"pith_short_12","alias_value":"VTA4YTNGEASR","created_at":"2026-07-05T08:53:07.258682+00:00"},{"alias_kind":"pith_short_16","alias_value":"VTA4YTNGEASRE7RY","created_at":"2026-07-05T08:53:07.258682+00:00"},{"alias_kind":"pith_short_8","alias_value":"VTA4YTNG","created_at":"2026-07-05T08:53:07.258682+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.00195","citing_title":"Algorithmic Collective Action with Two Collectives","ref_index":19,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VTA4YTNGEASRE7RY3UAIJ3B5UX","json":"https://pith.science/pith/VTA4YTNGEASRE7RY3UAIJ3B5UX.json","graph_json":"https://pith.science/api/pith-number/VTA4YTNGEASRE7RY3UAIJ3B5UX/graph.json","events_json":"https://pith.science/api/pith-number/VTA4YTNGEASRE7RY3UAIJ3B5UX/events.json","paper":"https://pith.science/paper/VTA4YTNG"},"agent_actions":{"view_html":"https://pith.science/pith/VTA4YTNGEASRE7RY3UAIJ3B5UX","download_json":"https://pith.science/pith/VTA4YTNGEASRE7RY3UAIJ3B5UX.json","view_paper":"https://pith.science/paper/VTA4YTNG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.04262&json=true","fetch_graph":"https://pith.science/api/pith-number/VTA4YTNGEASRE7RY3UAIJ3B5UX/graph.json","fetch_events":"https://pith.science/api/pith-number/VTA4YTNGEASRE7RY3UAIJ3B5UX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VTA4YTNGEASRE7RY3UAIJ3B5UX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VTA4YTNGEASRE7RY3UAIJ3B5UX/action/storage_attestation","attest_author":"https://pith.science/pith/VTA4YTNGEASRE7RY3UAIJ3B5UX/action/author_attestation","sign_citation":"https://pith.science/pith/VTA4YTNGEASRE7RY3UAIJ3B5UX/action/citation_signature","submit_replication":"https://pith.science/pith/VTA4YTNGEASRE7RY3UAIJ3B5UX/action/replication_record"}},"created_at":"2026-07-05T08:53:07.258682+00:00","updated_at":"2026-07-05T08:53:07.258682+00:00"}