{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:MW3AXHJZLZU5I2AKKF64IX4LYA","short_pith_number":"pith:MW3AXHJZ","schema_version":"1.0","canonical_sha256":"65b60b9d395e69d4680a517dc45f8bc0056d2f5f810d11523dd1e03270d33782","source":{"kind":"arxiv","id":"1812.08999","version":2},"attestation_state":"computed","paper":{"title":"Feature-Wise Bias Amplification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Anupam Datta, Emily Black, Klas Leino, Matt Fredrikson, Shayak Sen","submitted_at":"2018-12-21T08:48:30Z","abstract_excerpt":"We study the phenomenon of bias amplification in classifiers, wherein a machine learning model learns to predict classes with a greater disparity than the underlying ground truth. We demonstrate that bias amplification can arise via an inductive bias in gradient descent methods that results in the overestimation of the importance of moderately-predictive \"weak\" features if insufficient training data is available. This overestimation gives rise to feature-wise bias amplification -- a previously unreported form of bias that can be traced back to the features of a trained model. Through analysis "},"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":"1812.08999","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-21T08:48:30Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"d2cef39b9ba186d89cef21d47020799f7316fe0a8857e3e65443421702969662","abstract_canon_sha256":"6056b4c4b204d33d8d7ffe5db3f90cf1521a4a6b7d2de2269cfc94488e990225"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:13:29.121831Z","signature_b64":"jGQeM0KR6nOKM6VtTXNep6dfk7AP1el36hVFRyUc7y+PsdUCjFFKf/QTp1OpJ+KApQK2/wX7dzQlPbm4v5/rBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"65b60b9d395e69d4680a517dc45f8bc0056d2f5f810d11523dd1e03270d33782","last_reissued_at":"2026-07-05T00:13:29.121405Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:13:29.121405Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Feature-Wise Bias Amplification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Anupam Datta, Emily Black, Klas Leino, Matt Fredrikson, Shayak Sen","submitted_at":"2018-12-21T08:48:30Z","abstract_excerpt":"We study the phenomenon of bias amplification in classifiers, wherein a machine learning model learns to predict classes with a greater disparity than the underlying ground truth. We demonstrate that bias amplification can arise via an inductive bias in gradient descent methods that results in the overestimation of the importance of moderately-predictive \"weak\" features if insufficient training data is available. This overestimation gives rise to feature-wise bias amplification -- a previously unreported form of bias that can be traced back to the features of a trained model. Through analysis "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.08999","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/1812.08999/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":"1812.08999","created_at":"2026-07-05T00:13:29.121460+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.08999v2","created_at":"2026-07-05T00:13:29.121460+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.08999","created_at":"2026-07-05T00:13:29.121460+00:00"},{"alias_kind":"pith_short_12","alias_value":"MW3AXHJZLZU5","created_at":"2026-07-05T00:13:29.121460+00:00"},{"alias_kind":"pith_short_16","alias_value":"MW3AXHJZLZU5I2AK","created_at":"2026-07-05T00:13:29.121460+00:00"},{"alias_kind":"pith_short_8","alias_value":"MW3AXHJZ","created_at":"2026-07-05T00:13:29.121460+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2602.23580","citing_title":"BRIDGE the Gap: Mitigating Bias Amplification in Automated Scoring of English Language Learners via Inter-group Data Augmentation","ref_index":15,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MW3AXHJZLZU5I2AKKF64IX4LYA","json":"https://pith.science/pith/MW3AXHJZLZU5I2AKKF64IX4LYA.json","graph_json":"https://pith.science/api/pith-number/MW3AXHJZLZU5I2AKKF64IX4LYA/graph.json","events_json":"https://pith.science/api/pith-number/MW3AXHJZLZU5I2AKKF64IX4LYA/events.json","paper":"https://pith.science/paper/MW3AXHJZ"},"agent_actions":{"view_html":"https://pith.science/pith/MW3AXHJZLZU5I2AKKF64IX4LYA","download_json":"https://pith.science/pith/MW3AXHJZLZU5I2AKKF64IX4LYA.json","view_paper":"https://pith.science/paper/MW3AXHJZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.08999&json=true","fetch_graph":"https://pith.science/api/pith-number/MW3AXHJZLZU5I2AKKF64IX4LYA/graph.json","fetch_events":"https://pith.science/api/pith-number/MW3AXHJZLZU5I2AKKF64IX4LYA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MW3AXHJZLZU5I2AKKF64IX4LYA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MW3AXHJZLZU5I2AKKF64IX4LYA/action/storage_attestation","attest_author":"https://pith.science/pith/MW3AXHJZLZU5I2AKKF64IX4LYA/action/author_attestation","sign_citation":"https://pith.science/pith/MW3AXHJZLZU5I2AKKF64IX4LYA/action/citation_signature","submit_replication":"https://pith.science/pith/MW3AXHJZLZU5I2AKKF64IX4LYA/action/replication_record"}},"created_at":"2026-07-05T00:13:29.121460+00:00","updated_at":"2026-07-05T00:13:29.121460+00:00"}