{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:72AQGC4IBDFWD3G423U5JNHL7R","short_pith_number":"pith:72AQGC4I","schema_version":"1.0","canonical_sha256":"fe81030b8808cb61ecdcd6e9d4b4ebfc73cc0c8c8e92bb9318870631e7b76971","source":{"kind":"arxiv","id":"2501.08155","version":1},"attestation_state":"computed","paper":{"title":"FairTTTS: A Tree Test Time Simulation Method for Fairness-Aware Classification","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Bracha Shapira, Lior Rokach, Nurit Cohen-Inger, Seffi Cohen","submitted_at":"2025-01-14T14:29:36Z","abstract_excerpt":"Algorithmic decision-making has become deeply ingrained in many domains, yet biases in machine learning models can still produce discriminatory outcomes, often harming unprivileged groups. Achieving fair classification is inherently challenging, requiring a careful balance between predictive performance and ethical considerations. We present FairTTTS, a novel post-processing bias mitigation method inspired by the Tree Test Time Simulation (TTTS) method. Originally developed to enhance accuracy and robustness against adversarial inputs through probabilistic decision-path adjustments, TTTS serve"},"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":"2501.08155","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-14T14:29:36Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ae0c77fcfd95518f11be6ce960f7e075d7b4b0294090eabc3f0a9a472ffa4ada","abstract_canon_sha256":"93c81621e26c8607fed856b23e844df2b371c72319ffb03a74bc34d727aab38b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:00:56.770744Z","signature_b64":"1km32KUYgbMz8bXFXqHhXeMTXeHSpEQs4+2SjAsuu2P3nSXo4PZ2H9kn+20pi3WPzQq+M92iM97/RLUQ11ObCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe81030b8808cb61ecdcd6e9d4b4ebfc73cc0c8c8e92bb9318870631e7b76971","last_reissued_at":"2026-07-05T10:00:56.770322Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:00:56.770322Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FairTTTS: A Tree Test Time Simulation Method for Fairness-Aware Classification","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Bracha Shapira, Lior Rokach, Nurit Cohen-Inger, Seffi Cohen","submitted_at":"2025-01-14T14:29:36Z","abstract_excerpt":"Algorithmic decision-making has become deeply ingrained in many domains, yet biases in machine learning models can still produce discriminatory outcomes, often harming unprivileged groups. Achieving fair classification is inherently challenging, requiring a careful balance between predictive performance and ethical considerations. We present FairTTTS, a novel post-processing bias mitigation method inspired by the Tree Test Time Simulation (TTTS) method. Originally developed to enhance accuracy and robustness against adversarial inputs through probabilistic decision-path adjustments, TTTS serve"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.08155","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/2501.08155/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":"2501.08155","created_at":"2026-07-05T10:00:56.770381+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.08155v1","created_at":"2026-07-05T10:00:56.770381+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.08155","created_at":"2026-07-05T10:00:56.770381+00:00"},{"alias_kind":"pith_short_12","alias_value":"72AQGC4IBDFW","created_at":"2026-07-05T10:00:56.770381+00:00"},{"alias_kind":"pith_short_16","alias_value":"72AQGC4IBDFWD3G4","created_at":"2026-07-05T10:00:56.770381+00:00"},{"alias_kind":"pith_short_8","alias_value":"72AQGC4I","created_at":"2026-07-05T10:00:56.770381+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/72AQGC4IBDFWD3G423U5JNHL7R","json":"https://pith.science/pith/72AQGC4IBDFWD3G423U5JNHL7R.json","graph_json":"https://pith.science/api/pith-number/72AQGC4IBDFWD3G423U5JNHL7R/graph.json","events_json":"https://pith.science/api/pith-number/72AQGC4IBDFWD3G423U5JNHL7R/events.json","paper":"https://pith.science/paper/72AQGC4I"},"agent_actions":{"view_html":"https://pith.science/pith/72AQGC4IBDFWD3G423U5JNHL7R","download_json":"https://pith.science/pith/72AQGC4IBDFWD3G423U5JNHL7R.json","view_paper":"https://pith.science/paper/72AQGC4I","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.08155&json=true","fetch_graph":"https://pith.science/api/pith-number/72AQGC4IBDFWD3G423U5JNHL7R/graph.json","fetch_events":"https://pith.science/api/pith-number/72AQGC4IBDFWD3G423U5JNHL7R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/72AQGC4IBDFWD3G423U5JNHL7R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/72AQGC4IBDFWD3G423U5JNHL7R/action/storage_attestation","attest_author":"https://pith.science/pith/72AQGC4IBDFWD3G423U5JNHL7R/action/author_attestation","sign_citation":"https://pith.science/pith/72AQGC4IBDFWD3G423U5JNHL7R/action/citation_signature","submit_replication":"https://pith.science/pith/72AQGC4IBDFWD3G423U5JNHL7R/action/replication_record"}},"created_at":"2026-07-05T10:00:56.770381+00:00","updated_at":"2026-07-05T10:00:56.770381+00:00"}