{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:6Z6TA7H4KDIDV4AP4FTLKGMFPU","short_pith_number":"pith:6Z6TA7H4","schema_version":"1.0","canonical_sha256":"f67d307cfc50d03af00fe166b519857d3b79e2fcdc942974cc76908969405c96","source":{"kind":"arxiv","id":"2312.16191","version":1},"attestation_state":"computed","paper":{"title":"SoK: Taming the Triangle -- On the Interplays between Fairness, Interpretability and Privacy in Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Julien Ferry (LAAS-ROC), Marie-Jos\\'e Huguet (LAAS-ROC), Mohamed Siala (LAAS-ROC), S\\'ebastien Gambs (UQAM), Ulrich A\\\"ivodji (ETS)","submitted_at":"2023-12-22T08:11:33Z","abstract_excerpt":"Machine learning techniques are increasingly used for high-stakes decision-making, such as college admissions, loan attribution or recidivism prediction. Thus, it is crucial to ensure that the models learnt can be audited or understood by human users, do not create or reproduce discrimination or bias, and do not leak sensitive information regarding their training data. Indeed, interpretability, fairness and privacy are key requirements for the development of responsible machine learning, and all three have been studied extensively during the last decade. However, they were mainly considered in"},"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":"2312.16191","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-12-22T08:11:33Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"2a76f709f07564495a3c992902a656fb1d36b9b0aa5b9c32ea7f454a3dea10da","abstract_canon_sha256":"f8400583eac82a999311ed59a45d667e2ee3590d3d219875d1457455f2bae1dd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:28:14.020878Z","signature_b64":"sxQNX1cTn0jSgWU46nn5rnj8RlmylOtr//2qLNrwkV9rPEc3zawHqPtAVTc9ONckPnOzFJqP/mMBWEm5MYE1DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f67d307cfc50d03af00fe166b519857d3b79e2fcdc942974cc76908969405c96","last_reissued_at":"2026-07-05T07:28:14.020482Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:28:14.020482Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SoK: Taming the Triangle -- On the Interplays between Fairness, Interpretability and Privacy in Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Julien Ferry (LAAS-ROC), Marie-Jos\\'e Huguet (LAAS-ROC), Mohamed Siala (LAAS-ROC), S\\'ebastien Gambs (UQAM), Ulrich A\\\"ivodji (ETS)","submitted_at":"2023-12-22T08:11:33Z","abstract_excerpt":"Machine learning techniques are increasingly used for high-stakes decision-making, such as college admissions, loan attribution or recidivism prediction. Thus, it is crucial to ensure that the models learnt can be audited or understood by human users, do not create or reproduce discrimination or bias, and do not leak sensitive information regarding their training data. Indeed, interpretability, fairness and privacy are key requirements for the development of responsible machine learning, and all three have been studied extensively during the last decade. However, they were mainly considered in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.16191","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/2312.16191/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":"2312.16191","created_at":"2026-07-05T07:28:14.020540+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.16191v1","created_at":"2026-07-05T07:28:14.020540+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.16191","created_at":"2026-07-05T07:28:14.020540+00:00"},{"alias_kind":"pith_short_12","alias_value":"6Z6TA7H4KDID","created_at":"2026-07-05T07:28:14.020540+00:00"},{"alias_kind":"pith_short_16","alias_value":"6Z6TA7H4KDIDV4AP","created_at":"2026-07-05T07:28:14.020540+00:00"},{"alias_kind":"pith_short_8","alias_value":"6Z6TA7H4","created_at":"2026-07-05T07:28:14.020540+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.10091","citing_title":"SoK: Colluding Adversaries in Machine Learning Pipelines","ref_index":35,"is_internal_anchor":false},{"citing_arxiv_id":"2505.05707","citing_title":"Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacy","ref_index":11,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6Z6TA7H4KDIDV4AP4FTLKGMFPU","json":"https://pith.science/pith/6Z6TA7H4KDIDV4AP4FTLKGMFPU.json","graph_json":"https://pith.science/api/pith-number/6Z6TA7H4KDIDV4AP4FTLKGMFPU/graph.json","events_json":"https://pith.science/api/pith-number/6Z6TA7H4KDIDV4AP4FTLKGMFPU/events.json","paper":"https://pith.science/paper/6Z6TA7H4"},"agent_actions":{"view_html":"https://pith.science/pith/6Z6TA7H4KDIDV4AP4FTLKGMFPU","download_json":"https://pith.science/pith/6Z6TA7H4KDIDV4AP4FTLKGMFPU.json","view_paper":"https://pith.science/paper/6Z6TA7H4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.16191&json=true","fetch_graph":"https://pith.science/api/pith-number/6Z6TA7H4KDIDV4AP4FTLKGMFPU/graph.json","fetch_events":"https://pith.science/api/pith-number/6Z6TA7H4KDIDV4AP4FTLKGMFPU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6Z6TA7H4KDIDV4AP4FTLKGMFPU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6Z6TA7H4KDIDV4AP4FTLKGMFPU/action/storage_attestation","attest_author":"https://pith.science/pith/6Z6TA7H4KDIDV4AP4FTLKGMFPU/action/author_attestation","sign_citation":"https://pith.science/pith/6Z6TA7H4KDIDV4AP4FTLKGMFPU/action/citation_signature","submit_replication":"https://pith.science/pith/6Z6TA7H4KDIDV4AP4FTLKGMFPU/action/replication_record"}},"created_at":"2026-07-05T07:28:14.020540+00:00","updated_at":"2026-07-05T07:28:14.020540+00:00"}