{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:HVWUUKSKCRLQYNDQJLOT4SAIQO","short_pith_number":"pith:HVWUUKSK","schema_version":"1.0","canonical_sha256":"3d6d4a2a4a14570c34704add3e480883bbe630b0bf3af4d1649c66e465748e1a","source":{"kind":"arxiv","id":"2209.05889","version":1},"attestation_state":"computed","paper":{"title":"Investigating Bias with a Synthetic Data Generator: Empirical Evidence and Philosophical Interpretation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CY","cs.LG"],"primary_cat":"stat.ML","authors_text":"Alessandro Castelnovo, Andrea Cosentini, Daniele Regoli, Nicole Inverardi, Riccardo Crupi","submitted_at":"2022-09-13T11:18:50Z","abstract_excerpt":"Machine learning applications are becoming increasingly pervasive in our society. Since these decision-making systems rely on data-driven learning, risk is that they will systematically spread the bias embedded in data. In this paper, we propose to analyze biases by introducing a framework for generating synthetic data with specific types of bias and their combinations. We delve into the nature of these biases discussing their relationship to moral and justice frameworks. Finally, we exploit our proposed synthetic data generator to perform experiments on different scenarios, with various bias "},"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":"2209.05889","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2022-09-13T11:18:50Z","cross_cats_sorted":["cs.AI","cs.CY","cs.LG"],"title_canon_sha256":"c78578914644992e5b39c30e4be38f4b1e82ff18f02f71b107c9eb478734d243","abstract_canon_sha256":"11cb3760781137c25b38b944c20d5cf036743934580a47c51f8a51b3817efabe"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:39:53.130728Z","signature_b64":"YIXjG8ULv+Jk6abjazL5i1HMESIj1SdFXdklKpl7nZ9FpBUe+C/JmBxVpMhYgJyfwzAPkJYYl9HxFg37cILgBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3d6d4a2a4a14570c34704add3e480883bbe630b0bf3af4d1649c66e465748e1a","last_reissued_at":"2026-07-05T05:39:53.130383Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:39:53.130383Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Investigating Bias with a Synthetic Data Generator: Empirical Evidence and Philosophical Interpretation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CY","cs.LG"],"primary_cat":"stat.ML","authors_text":"Alessandro Castelnovo, Andrea Cosentini, Daniele Regoli, Nicole Inverardi, Riccardo Crupi","submitted_at":"2022-09-13T11:18:50Z","abstract_excerpt":"Machine learning applications are becoming increasingly pervasive in our society. Since these decision-making systems rely on data-driven learning, risk is that they will systematically spread the bias embedded in data. In this paper, we propose to analyze biases by introducing a framework for generating synthetic data with specific types of bias and their combinations. We delve into the nature of these biases discussing their relationship to moral and justice frameworks. Finally, we exploit our proposed synthetic data generator to perform experiments on different scenarios, with various bias "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.05889","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/2209.05889/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":"2209.05889","created_at":"2026-07-05T05:39:53.130437+00:00"},{"alias_kind":"arxiv_version","alias_value":"2209.05889v1","created_at":"2026-07-05T05:39:53.130437+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.05889","created_at":"2026-07-05T05:39:53.130437+00:00"},{"alias_kind":"pith_short_12","alias_value":"HVWUUKSKCRLQ","created_at":"2026-07-05T05:39:53.130437+00:00"},{"alias_kind":"pith_short_16","alias_value":"HVWUUKSKCRLQYNDQ","created_at":"2026-07-05T05:39:53.130437+00:00"},{"alias_kind":"pith_short_8","alias_value":"HVWUUKSK","created_at":"2026-07-05T05:39:53.130437+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/HVWUUKSKCRLQYNDQJLOT4SAIQO","json":"https://pith.science/pith/HVWUUKSKCRLQYNDQJLOT4SAIQO.json","graph_json":"https://pith.science/api/pith-number/HVWUUKSKCRLQYNDQJLOT4SAIQO/graph.json","events_json":"https://pith.science/api/pith-number/HVWUUKSKCRLQYNDQJLOT4SAIQO/events.json","paper":"https://pith.science/paper/HVWUUKSK"},"agent_actions":{"view_html":"https://pith.science/pith/HVWUUKSKCRLQYNDQJLOT4SAIQO","download_json":"https://pith.science/pith/HVWUUKSKCRLQYNDQJLOT4SAIQO.json","view_paper":"https://pith.science/paper/HVWUUKSK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2209.05889&json=true","fetch_graph":"https://pith.science/api/pith-number/HVWUUKSKCRLQYNDQJLOT4SAIQO/graph.json","fetch_events":"https://pith.science/api/pith-number/HVWUUKSKCRLQYNDQJLOT4SAIQO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HVWUUKSKCRLQYNDQJLOT4SAIQO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HVWUUKSKCRLQYNDQJLOT4SAIQO/action/storage_attestation","attest_author":"https://pith.science/pith/HVWUUKSKCRLQYNDQJLOT4SAIQO/action/author_attestation","sign_citation":"https://pith.science/pith/HVWUUKSKCRLQYNDQJLOT4SAIQO/action/citation_signature","submit_replication":"https://pith.science/pith/HVWUUKSKCRLQYNDQJLOT4SAIQO/action/replication_record"}},"created_at":"2026-07-05T05:39:53.130437+00:00","updated_at":"2026-07-05T05:39:53.130437+00:00"}