{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:CGZ3UKUOM6DYC2Y4LPNASKGWO6","short_pith_number":"pith:CGZ3UKUO","schema_version":"1.0","canonical_sha256":"11b3ba2a8e6787816b1c5bda0928d677adc2b41feb2128b7f7fbd5c4c57266fb","source":{"kind":"arxiv","id":"2603.05582","version":2},"attestation_state":"computed","paper":{"title":"Bias In, Bias Out? Finding Unbiased Subnetworks in Vanilla Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Standard neural networks trained on biased data already contain unbiased subnetworks that can be isolated by pruning without retraining.","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Abdel Djalil Sad Saoud, Ekaterina Iakovleva, Enzo Tartaglione, Ivan Luiz De Moura Matos, Vito Paolo Pastore","submitted_at":"2026-03-05T18:54:24Z","abstract_excerpt":"The issue of algorithmic biases in deep learning has led to the development of various debiasing techniques, many of which perform complex training procedures or dataset manipulation. However, an intriguing question arises: is it possible to extract fair and bias-agnostic subnetworks from standard vanilla-trained models without relying on additional data, such as unbiased training set? In this work, we introduce Bias-Invariant Subnetwork Extraction (BISE), a learning strategy that identifies and isolates \"bias-free\" subnetworks that already exist within conventionally trained models, without r"},"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":true,"formal_links_present":false},"canonical_record":{"source":{"id":"2603.05582","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-03-05T18:54:24Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"73ad39a2088fcc1504007edb5e13d51b91ac084245ac97158a4d3a401c11e2c7","abstract_canon_sha256":"f762874fdb892df5881b6aa267b02aee7436d9a68466bbbede61cf3b90da3be9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:09:23.073387Z","signature_b64":"bFRB5ZluoszPZcHI9JwmfvOByVkf/2dGqom7uAJePkKbmjGt9HAPQ9fRvTpxw3mQqzrf/UHDqmWdN8Jj01OgBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"11b3ba2a8e6787816b1c5bda0928d677adc2b41feb2128b7f7fbd5c4c57266fb","last_reissued_at":"2026-05-18T03:09:23.072596Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:09:23.072596Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bias In, Bias Out? Finding Unbiased Subnetworks in Vanilla Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Standard neural networks trained on biased data already contain unbiased subnetworks that can be isolated by pruning without retraining.","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Abdel Djalil Sad Saoud, Ekaterina Iakovleva, Enzo Tartaglione, Ivan Luiz De Moura Matos, Vito Paolo Pastore","submitted_at":"2026-03-05T18:54:24Z","abstract_excerpt":"The issue of algorithmic biases in deep learning has led to the development of various debiasing techniques, many of which perform complex training procedures or dataset manipulation. However, an intriguing question arises: is it possible to extract fair and bias-agnostic subnetworks from standard vanilla-trained models without relying on additional data, such as unbiased training set? In this work, we introduce Bias-Invariant Subnetwork Extraction (BISE), a learning strategy that identifies and isolates \"bias-free\" subnetworks that already exist within conventionally trained models, without r"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"such subnetworks can be extracted via pruning and can operate without modification, effectively relying less on biased features and maintaining robust performance.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That bias-free subnetworks already exist within conventionally trained models and can be reliably identified and isolated by pruning without any additional unbiased data or retraining.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"BISE extracts bias-free subnetworks from conventionally trained models via pruning, enabling debiased operation without retraining or additional data.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Standard neural networks trained on biased data already contain unbiased subnetworks that can be isolated by pruning without retraining.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"31d3beb35c4adf966820e222354dfdc222b49edf4c9918fbd37d1790bee830e8"},"source":{"id":"2603.05582","kind":"arxiv","version":2},"verdict":{"id":"fc315518-44e6-4fd3-9291-04fb0e1dd2b1","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T16:09:50.791851Z","strongest_claim":"such subnetworks can be extracted via pruning and can operate without modification, effectively relying less on biased features and maintaining robust performance.","one_line_summary":"BISE extracts bias-free subnetworks from conventionally trained models via pruning, enabling debiased operation without retraining or additional data.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That bias-free subnetworks already exist within conventionally trained models and can be reliably identified and isolated by pruning without any additional unbiased data or retraining.","pith_extraction_headline":"Standard neural networks trained on biased data already contain unbiased subnetworks that can be isolated by pruning without retraining."},"references":{"count":114,"sample":[{"doi":"","year":2024,"title":"The EU artificial intelligence act, 2024","work_id":"19159a04-7247-40a3-8b8b-09ec0d54601f","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"Does data repair lead to fair models? curating con- textually fair data to reduce model bias","work_id":"7ff66c82-b7ac-4516-8f92-db01b5d117f8","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"Systematic generalisation with group in- variant predictions","work_id":"565bc9b5-708b-45ba-80d9-7e30479470ac","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Mind the gap: Challenges of deep learning approaches to theory of mind.Artificial Intelligence Review, 2023","work_id":"73fc3e14-6476-4e92-8389-b32d6a9c76d1","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2020,"title":"Learning de-biased represen- tations with biased representations","work_id":"385b52bb-2a42-468d-a8a3-0d14464a1d6f","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":114,"snapshot_sha256":"e647121feccf727c930911bf91b9e16d1f28fa92c807a296bc5e1e0fd44ad599","internal_anchors":2},"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":"2603.05582","created_at":"2026-05-18T03:09:23.072738+00:00"},{"alias_kind":"arxiv_version","alias_value":"2603.05582v2","created_at":"2026-05-18T03:09:23.072738+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2603.05582","created_at":"2026-05-18T03:09:23.072738+00:00"},{"alias_kind":"pith_short_12","alias_value":"CGZ3UKUOM6DY","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_16","alias_value":"CGZ3UKUOM6DYC2Y4","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_8","alias_value":"CGZ3UKUO","created_at":"2026-05-18T12:33:37.589309+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/CGZ3UKUOM6DYC2Y4LPNASKGWO6","json":"https://pith.science/pith/CGZ3UKUOM6DYC2Y4LPNASKGWO6.json","graph_json":"https://pith.science/api/pith-number/CGZ3UKUOM6DYC2Y4LPNASKGWO6/graph.json","events_json":"https://pith.science/api/pith-number/CGZ3UKUOM6DYC2Y4LPNASKGWO6/events.json","paper":"https://pith.science/paper/CGZ3UKUO"},"agent_actions":{"view_html":"https://pith.science/pith/CGZ3UKUOM6DYC2Y4LPNASKGWO6","download_json":"https://pith.science/pith/CGZ3UKUOM6DYC2Y4LPNASKGWO6.json","view_paper":"https://pith.science/paper/CGZ3UKUO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2603.05582&json=true","fetch_graph":"https://pith.science/api/pith-number/CGZ3UKUOM6DYC2Y4LPNASKGWO6/graph.json","fetch_events":"https://pith.science/api/pith-number/CGZ3UKUOM6DYC2Y4LPNASKGWO6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CGZ3UKUOM6DYC2Y4LPNASKGWO6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CGZ3UKUOM6DYC2Y4LPNASKGWO6/action/storage_attestation","attest_author":"https://pith.science/pith/CGZ3UKUOM6DYC2Y4LPNASKGWO6/action/author_attestation","sign_citation":"https://pith.science/pith/CGZ3UKUOM6DYC2Y4LPNASKGWO6/action/citation_signature","submit_replication":"https://pith.science/pith/CGZ3UKUOM6DYC2Y4LPNASKGWO6/action/replication_record"}},"created_at":"2026-05-18T03:09:23.072738+00:00","updated_at":"2026-05-18T03:09:23.072738+00:00"}