{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:UXJEPIVTPGBLANEE332R55TMGK","short_pith_number":"pith:UXJEPIVT","schema_version":"1.0","canonical_sha256":"a5d247a2b37982b03484def51ef66c32a6ac6af1ad3a5263edb9a49ececde2a9","source":{"kind":"arxiv","id":"1809.09245","version":1},"attestation_state":"computed","paper":{"title":"Evaluating Fairness Metrics in the Presence of Dataset Bias","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"J. Henry Hinnefeld, Nat Mammo, Peter Cooman, Rupert Deese","submitted_at":"2018-09-24T22:32:05Z","abstract_excerpt":"Data-driven algorithms play a large role in decision making across a variety of industries. Increasingly, these algorithms are being used to make decisions that have significant ramifications for people's social and economic well-being, e.g. in sentencing, loan approval, and policing. Amid the proliferation of such systems there is a growing concern about their potential discriminatory impact. In particular, machine learning systems which are trained on biased data have the potential to learn and perpetuate those biases. A central challenge for practitioners is thus to determine whether their "},"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":"1809.09245","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-24T22:32:05Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"c943c23e1fcfeddb5ea6cb4740d921027940385b44eb5e74ac769eff69c2dfa2","abstract_canon_sha256":"201367bd3f34ca2bb77e161e4b00dc2a286022753e203219fa6e82473c68f6be"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:04:53.958538Z","signature_b64":"R5hjdzuwwXjY8RqINtZ9h5zggr2djyNKlf2Ca73YNfIL7JpJr4+peVT8F92ee6IJMXi8wWyZaZqxnyOzYY3YAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a5d247a2b37982b03484def51ef66c32a6ac6af1ad3a5263edb9a49ececde2a9","last_reissued_at":"2026-05-18T00:04:53.957905Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:04:53.957905Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evaluating Fairness Metrics in the Presence of Dataset Bias","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"J. Henry Hinnefeld, Nat Mammo, Peter Cooman, Rupert Deese","submitted_at":"2018-09-24T22:32:05Z","abstract_excerpt":"Data-driven algorithms play a large role in decision making across a variety of industries. Increasingly, these algorithms are being used to make decisions that have significant ramifications for people's social and economic well-being, e.g. in sentencing, loan approval, and policing. Amid the proliferation of such systems there is a growing concern about their potential discriminatory impact. In particular, machine learning systems which are trained on biased data have the potential to learn and perpetuate those biases. A central challenge for practitioners is thus to determine whether their "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.09245","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":""},"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":"1809.09245","created_at":"2026-05-18T00:04:53.958026+00:00"},{"alias_kind":"arxiv_version","alias_value":"1809.09245v1","created_at":"2026-05-18T00:04:53.958026+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.09245","created_at":"2026-05-18T00:04:53.958026+00:00"},{"alias_kind":"pith_short_12","alias_value":"UXJEPIVTPGBL","created_at":"2026-05-18T12:32:56.356000+00:00"},{"alias_kind":"pith_short_16","alias_value":"UXJEPIVTPGBLANEE","created_at":"2026-05-18T12:32:56.356000+00:00"},{"alias_kind":"pith_short_8","alias_value":"UXJEPIVT","created_at":"2026-05-18T12:32:56.356000+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/UXJEPIVTPGBLANEE332R55TMGK","json":"https://pith.science/pith/UXJEPIVTPGBLANEE332R55TMGK.json","graph_json":"https://pith.science/api/pith-number/UXJEPIVTPGBLANEE332R55TMGK/graph.json","events_json":"https://pith.science/api/pith-number/UXJEPIVTPGBLANEE332R55TMGK/events.json","paper":"https://pith.science/paper/UXJEPIVT"},"agent_actions":{"view_html":"https://pith.science/pith/UXJEPIVTPGBLANEE332R55TMGK","download_json":"https://pith.science/pith/UXJEPIVTPGBLANEE332R55TMGK.json","view_paper":"https://pith.science/paper/UXJEPIVT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1809.09245&json=true","fetch_graph":"https://pith.science/api/pith-number/UXJEPIVTPGBLANEE332R55TMGK/graph.json","fetch_events":"https://pith.science/api/pith-number/UXJEPIVTPGBLANEE332R55TMGK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UXJEPIVTPGBLANEE332R55TMGK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UXJEPIVTPGBLANEE332R55TMGK/action/storage_attestation","attest_author":"https://pith.science/pith/UXJEPIVTPGBLANEE332R55TMGK/action/author_attestation","sign_citation":"https://pith.science/pith/UXJEPIVTPGBLANEE332R55TMGK/action/citation_signature","submit_replication":"https://pith.science/pith/UXJEPIVTPGBLANEE332R55TMGK/action/replication_record"}},"created_at":"2026-05-18T00:04:53.958026+00:00","updated_at":"2026-05-18T00:04:53.958026+00:00"}