{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:PJ3IBUERZGTQ66QUKJDCUCKBDL","short_pith_number":"pith:PJ3IBUER","schema_version":"1.0","canonical_sha256":"7a7680d091c9a70f7a1452462a09411af19145eebb5d8bef2d2e0b9f53ef5f60","source":{"kind":"arxiv","id":"2010.07389","version":1},"attestation_state":"computed","paper":{"title":"Explainability for fair machine learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Christopher Frye, Ilya Feige, Tobias Schwedes, Tom Begley","submitted_at":"2020-10-14T20:21:01Z","abstract_excerpt":"As the decisions made or influenced by machine learning models increasingly impact our lives, it is crucial to detect, understand, and mitigate unfairness. But even simply determining what \"unfairness\" should mean in a given context is non-trivial: there are many competing definitions, and choosing between them often requires a deep understanding of the underlying task. It is thus tempting to use model explainability to gain insights into model fairness, however existing explainability tools do not reliably indicate whether a model is indeed fair. In this work we present a new approach to expl"},"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":"2010.07389","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-14T20:21:01Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"897c3b998887a80b77d76349b1205f1c03b50cadd1a7b22ab7b574b420aaf937","abstract_canon_sha256":"20507feb9e6320e9b6f8bb2f193fdfca0566be809cc7518fafb45e44a9566378"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:43:06.710785Z","signature_b64":"ux4TFSHBqStLLB2zpO8nPqBrsXh+m4PZbxHxd7zaiZ4GzYaoD356vl06XQDK3AN5HQ7CUnshz6KP03OsNfxAAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7a7680d091c9a70f7a1452462a09411af19145eebb5d8bef2d2e0b9f53ef5f60","last_reissued_at":"2026-07-05T01:43:06.710228Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:43:06.710228Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Explainability for fair machine learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Christopher Frye, Ilya Feige, Tobias Schwedes, Tom Begley","submitted_at":"2020-10-14T20:21:01Z","abstract_excerpt":"As the decisions made or influenced by machine learning models increasingly impact our lives, it is crucial to detect, understand, and mitigate unfairness. But even simply determining what \"unfairness\" should mean in a given context is non-trivial: there are many competing definitions, and choosing between them often requires a deep understanding of the underlying task. It is thus tempting to use model explainability to gain insights into model fairness, however existing explainability tools do not reliably indicate whether a model is indeed fair. In this work we present a new approach to expl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.07389","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2010.07389/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":"2010.07389","created_at":"2026-07-05T01:43:06.710324+00:00"},{"alias_kind":"arxiv_version","alias_value":"2010.07389v1","created_at":"2026-07-05T01:43:06.710324+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.07389","created_at":"2026-07-05T01:43:06.710324+00:00"},{"alias_kind":"pith_short_12","alias_value":"PJ3IBUERZGTQ","created_at":"2026-07-05T01:43:06.710324+00:00"},{"alias_kind":"pith_short_16","alias_value":"PJ3IBUERZGTQ66QU","created_at":"2026-07-05T01:43:06.710324+00:00"},{"alias_kind":"pith_short_8","alias_value":"PJ3IBUER","created_at":"2026-07-05T01:43:06.710324+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2603.13452","citing_title":"MESD: A Risk-Sensitive Metric for Explanation Fairness Across Intersectional Subgroups","ref_index":25,"is_internal_anchor":false},{"citing_arxiv_id":"2603.13452","citing_title":"MESD: A Risk-Sensitive Metric for Explanation Fairness Across Intersectional Subgroups","ref_index":25,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12701","citing_title":"Do Fair Models Reason Fairly? Counterfactual Explanation Consistency for Procedural Fairness in Credit Decisions","ref_index":35,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09852","citing_title":"Fairness of Explanations in Artificial Intelligence (AI): A Unifying Framework, Axioms, and Future Direction toward Responsible AI","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PJ3IBUERZGTQ66QUKJDCUCKBDL","json":"https://pith.science/pith/PJ3IBUERZGTQ66QUKJDCUCKBDL.json","graph_json":"https://pith.science/api/pith-number/PJ3IBUERZGTQ66QUKJDCUCKBDL/graph.json","events_json":"https://pith.science/api/pith-number/PJ3IBUERZGTQ66QUKJDCUCKBDL/events.json","paper":"https://pith.science/paper/PJ3IBUER"},"agent_actions":{"view_html":"https://pith.science/pith/PJ3IBUERZGTQ66QUKJDCUCKBDL","download_json":"https://pith.science/pith/PJ3IBUERZGTQ66QUKJDCUCKBDL.json","view_paper":"https://pith.science/paper/PJ3IBUER","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2010.07389&json=true","fetch_graph":"https://pith.science/api/pith-number/PJ3IBUERZGTQ66QUKJDCUCKBDL/graph.json","fetch_events":"https://pith.science/api/pith-number/PJ3IBUERZGTQ66QUKJDCUCKBDL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PJ3IBUERZGTQ66QUKJDCUCKBDL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PJ3IBUERZGTQ66QUKJDCUCKBDL/action/storage_attestation","attest_author":"https://pith.science/pith/PJ3IBUERZGTQ66QUKJDCUCKBDL/action/author_attestation","sign_citation":"https://pith.science/pith/PJ3IBUERZGTQ66QUKJDCUCKBDL/action/citation_signature","submit_replication":"https://pith.science/pith/PJ3IBUERZGTQ66QUKJDCUCKBDL/action/replication_record"}},"created_at":"2026-07-05T01:43:06.710324+00:00","updated_at":"2026-07-05T01:43:06.710324+00:00"}