{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:DF34UVYES4QEH55FCXQI7OXXDA","short_pith_number":"pith:DF34UVYE","schema_version":"1.0","canonical_sha256":"1977ca5704972043f7a515e08fbaf7182a156f319d871c38bf7d4933082839eb","source":{"kind":"arxiv","id":"2409.14055","version":5},"attestation_state":"computed","paper":{"title":"Monitoring Human Dependence On AI Systems With Reliance Drills","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CY","authors_text":"Jamie Bernardi, Merlin Stein, Richard Moulange, Rosco Hunter","submitted_at":"2024-09-21T08:09:31Z","abstract_excerpt":"AI systems are assisting humans with increasingly diverse intellectual tasks but are still prone to mistakes. Humans are over-reliant on this assistance if they trust AI-generated advice, even though they would make a better decision on their own. To identify such instances of over-reliance, this paper proposes the reliance drill: an exercise that tests whether a human can recognise mistakes in AI-generated advice. Our paper examines the reasons why an organisation might choose to implement reliance drills and the doubts they may have about doing so. As an example, we consider the benefits and"},"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":"2409.14055","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CY","submitted_at":"2024-09-21T08:09:31Z","cross_cats_sorted":[],"title_canon_sha256":"356194c8f990208806faca818b613eef04aa0e5de509006775f9b124c1c76d9d","abstract_canon_sha256":"03bf3d6f10e1ab039261565fcab33908f2a35594a195ca8be6b87072e85930ec"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:41:42.156269Z","signature_b64":"fIWnuuxVTct9pf2AIrB9N+DImpewt/d71Op1KcqMhJE1Hjt0XFBYMT2hIzSE0aqbiTX3zxRnb8qLx8oyuOh8DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1977ca5704972043f7a515e08fbaf7182a156f319d871c38bf7d4933082839eb","last_reissued_at":"2026-07-05T09:41:42.155747Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:41:42.155747Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Monitoring Human Dependence On AI Systems With Reliance Drills","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CY","authors_text":"Jamie Bernardi, Merlin Stein, Richard Moulange, Rosco Hunter","submitted_at":"2024-09-21T08:09:31Z","abstract_excerpt":"AI systems are assisting humans with increasingly diverse intellectual tasks but are still prone to mistakes. Humans are over-reliant on this assistance if they trust AI-generated advice, even though they would make a better decision on their own. To identify such instances of over-reliance, this paper proposes the reliance drill: an exercise that tests whether a human can recognise mistakes in AI-generated advice. Our paper examines the reasons why an organisation might choose to implement reliance drills and the doubts they may have about doing so. As an example, we consider the benefits and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.14055","kind":"arxiv","version":5},"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/2409.14055/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":"2409.14055","created_at":"2026-07-05T09:41:42.155806+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.14055v5","created_at":"2026-07-05T09:41:42.155806+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.14055","created_at":"2026-07-05T09:41:42.155806+00:00"},{"alias_kind":"pith_short_12","alias_value":"DF34UVYES4QE","created_at":"2026-07-05T09:41:42.155806+00:00"},{"alias_kind":"pith_short_16","alias_value":"DF34UVYES4QEH55F","created_at":"2026-07-05T09:41:42.155806+00:00"},{"alias_kind":"pith_short_8","alias_value":"DF34UVYE","created_at":"2026-07-05T09:41:42.155806+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2509.08010","citing_title":"Measuring and mitigating overreliance to build human-compatible AI","ref_index":61,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DF34UVYES4QEH55FCXQI7OXXDA","json":"https://pith.science/pith/DF34UVYES4QEH55FCXQI7OXXDA.json","graph_json":"https://pith.science/api/pith-number/DF34UVYES4QEH55FCXQI7OXXDA/graph.json","events_json":"https://pith.science/api/pith-number/DF34UVYES4QEH55FCXQI7OXXDA/events.json","paper":"https://pith.science/paper/DF34UVYE"},"agent_actions":{"view_html":"https://pith.science/pith/DF34UVYES4QEH55FCXQI7OXXDA","download_json":"https://pith.science/pith/DF34UVYES4QEH55FCXQI7OXXDA.json","view_paper":"https://pith.science/paper/DF34UVYE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.14055&json=true","fetch_graph":"https://pith.science/api/pith-number/DF34UVYES4QEH55FCXQI7OXXDA/graph.json","fetch_events":"https://pith.science/api/pith-number/DF34UVYES4QEH55FCXQI7OXXDA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DF34UVYES4QEH55FCXQI7OXXDA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DF34UVYES4QEH55FCXQI7OXXDA/action/storage_attestation","attest_author":"https://pith.science/pith/DF34UVYES4QEH55FCXQI7OXXDA/action/author_attestation","sign_citation":"https://pith.science/pith/DF34UVYES4QEH55FCXQI7OXXDA/action/citation_signature","submit_replication":"https://pith.science/pith/DF34UVYES4QEH55FCXQI7OXXDA/action/replication_record"}},"created_at":"2026-07-05T09:41:42.155806+00:00","updated_at":"2026-07-05T09:41:42.155806+00:00"}