{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:UR76C7YE24BMZAWRIJANAJSP4P","short_pith_number":"pith:UR76C7YE","schema_version":"1.0","canonical_sha256":"a47fe17f04d702cc82d14240d0264fe3eff88b2cc7ff384f010460b02a82e21b","source":{"kind":"arxiv","id":"2305.18504","version":1},"attestation_state":"computed","paper":{"title":"Generalized Disparate Impact for Configurable Fairness Solutions in ML","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Eleonora Misino, Luca Giuliani, Michele Lombardi","submitted_at":"2023-05-29T14:57:38Z","abstract_excerpt":"We make two contributions in the field of AI fairness over continuous protected attributes. First, we show that the Hirschfeld-Gebelein-Renyi (HGR) indicator (the only one currently available for such a case) is valuable but subject to a few crucial limitations regarding semantics, interpretability, and robustness. Second, we introduce a family of indicators that are: 1) complementary to HGR in terms of semantics; 2) fully interpretable and transparent; 3) robust over finite samples; 4) configurable to suit specific applications. Our approach also allows us to define fine-grained constraints t"},"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":"2305.18504","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-29T14:57:38Z","cross_cats_sorted":[],"title_canon_sha256":"b74f998fcbbcc6b524b6d8f05a8fe1d4ef4e4e374be6e8b29852fda24a162e05","abstract_canon_sha256":"d562b8770d209b4f3199ca11c4cc1df1105379d790dac3156ce3768da6ddc30f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:15:01.791709Z","signature_b64":"ZqtKL2a1mEBybYxJNc+uIkM9aDYo2dk4CK4UKH98PvCuG2mtY1bvqEegfaxz+9U2KMPjsnRuGvL8nw6fjIHFDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a47fe17f04d702cc82d14240d0264fe3eff88b2cc7ff384f010460b02a82e21b","last_reissued_at":"2026-07-05T06:15:01.791292Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:15:01.791292Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generalized Disparate Impact for Configurable Fairness Solutions in ML","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Eleonora Misino, Luca Giuliani, Michele Lombardi","submitted_at":"2023-05-29T14:57:38Z","abstract_excerpt":"We make two contributions in the field of AI fairness over continuous protected attributes. First, we show that the Hirschfeld-Gebelein-Renyi (HGR) indicator (the only one currently available for such a case) is valuable but subject to a few crucial limitations regarding semantics, interpretability, and robustness. Second, we introduce a family of indicators that are: 1) complementary to HGR in terms of semantics; 2) fully interpretable and transparent; 3) robust over finite samples; 4) configurable to suit specific applications. Our approach also allows us to define fine-grained constraints t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.18504","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/2305.18504/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":"2305.18504","created_at":"2026-07-05T06:15:01.791354+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.18504v1","created_at":"2026-07-05T06:15:01.791354+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.18504","created_at":"2026-07-05T06:15:01.791354+00:00"},{"alias_kind":"pith_short_12","alias_value":"UR76C7YE24BM","created_at":"2026-07-05T06:15:01.791354+00:00"},{"alias_kind":"pith_short_16","alias_value":"UR76C7YE24BMZAWR","created_at":"2026-07-05T06:15:01.791354+00:00"},{"alias_kind":"pith_short_8","alias_value":"UR76C7YE","created_at":"2026-07-05T06:15:01.791354+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/UR76C7YE24BMZAWRIJANAJSP4P","json":"https://pith.science/pith/UR76C7YE24BMZAWRIJANAJSP4P.json","graph_json":"https://pith.science/api/pith-number/UR76C7YE24BMZAWRIJANAJSP4P/graph.json","events_json":"https://pith.science/api/pith-number/UR76C7YE24BMZAWRIJANAJSP4P/events.json","paper":"https://pith.science/paper/UR76C7YE"},"agent_actions":{"view_html":"https://pith.science/pith/UR76C7YE24BMZAWRIJANAJSP4P","download_json":"https://pith.science/pith/UR76C7YE24BMZAWRIJANAJSP4P.json","view_paper":"https://pith.science/paper/UR76C7YE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.18504&json=true","fetch_graph":"https://pith.science/api/pith-number/UR76C7YE24BMZAWRIJANAJSP4P/graph.json","fetch_events":"https://pith.science/api/pith-number/UR76C7YE24BMZAWRIJANAJSP4P/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UR76C7YE24BMZAWRIJANAJSP4P/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UR76C7YE24BMZAWRIJANAJSP4P/action/storage_attestation","attest_author":"https://pith.science/pith/UR76C7YE24BMZAWRIJANAJSP4P/action/author_attestation","sign_citation":"https://pith.science/pith/UR76C7YE24BMZAWRIJANAJSP4P/action/citation_signature","submit_replication":"https://pith.science/pith/UR76C7YE24BMZAWRIJANAJSP4P/action/replication_record"}},"created_at":"2026-07-05T06:15:01.791354+00:00","updated_at":"2026-07-05T06:15:01.791354+00:00"}