{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:3AGQYODF7QGVQQ572CSDJBAKLG","short_pith_number":"pith:3AGQYODF","schema_version":"1.0","canonical_sha256":"d80d0c3865fc0d5843bfd0a434840a598d86c5492c161717e108a3bda38fa873","source":{"kind":"arxiv","id":"2004.13102","version":3},"attestation_state":"computed","paper":{"title":"Is the Most Accurate AI the Best Teammate? Optimizing AI for Teamwork","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC","cs.LG"],"primary_cat":"cs.AI","authors_text":"Besmira Nushi, Daniel S. Weld, Ece Kamar, Eric Horvitz, Gagan Bansal","submitted_at":"2020-04-27T19:06:28Z","abstract_excerpt":"AI practitioners typically strive to develop the most accurate systems, making an implicit assumption that the AI system will function autonomously. However, in practice, AI systems often are used to provide advice to people in domains ranging from criminal justice and finance to healthcare. In such AI-advised decision making, humans and machines form a team, where the human is responsible for making final decisions. But is the most accurate AI the best teammate? We argue \"No\" -- predictable performance may be worth a slight sacrifice in AI accuracy. Instead, we argue that AI systems should be"},"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":"2004.13102","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2020-04-27T19:06:28Z","cross_cats_sorted":["cs.HC","cs.LG"],"title_canon_sha256":"bf1fab8943e9d56fe4cedbfdc34519291e128b0c0101fc1181a7885a5e753016","abstract_canon_sha256":"7110d2eafe66a9bd1689091717a9a20ae5334c493f6c420097834e16aebfd260"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:16:44.737177Z","signature_b64":"Qc1JrJ5LoDsjexBKNxnqR5AW9DRnmpDTHq34jf+fhCEWER95CJdwVFwl8tZtMwcJ3YQOqhuoB3967ykzj4iZBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d80d0c3865fc0d5843bfd0a434840a598d86c5492c161717e108a3bda38fa873","last_reissued_at":"2026-07-05T02:16:44.736765Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:16:44.736765Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Is the Most Accurate AI the Best Teammate? Optimizing AI for Teamwork","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC","cs.LG"],"primary_cat":"cs.AI","authors_text":"Besmira Nushi, Daniel S. Weld, Ece Kamar, Eric Horvitz, Gagan Bansal","submitted_at":"2020-04-27T19:06:28Z","abstract_excerpt":"AI practitioners typically strive to develop the most accurate systems, making an implicit assumption that the AI system will function autonomously. However, in practice, AI systems often are used to provide advice to people in domains ranging from criminal justice and finance to healthcare. In such AI-advised decision making, humans and machines form a team, where the human is responsible for making final decisions. But is the most accurate AI the best teammate? We argue \"No\" -- predictable performance may be worth a slight sacrifice in AI accuracy. Instead, we argue that AI systems should be"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.13102","kind":"arxiv","version":3},"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/2004.13102/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":"2004.13102","created_at":"2026-07-05T02:16:44.736823+00:00"},{"alias_kind":"arxiv_version","alias_value":"2004.13102v3","created_at":"2026-07-05T02:16:44.736823+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.13102","created_at":"2026-07-05T02:16:44.736823+00:00"},{"alias_kind":"pith_short_12","alias_value":"3AGQYODF7QGV","created_at":"2026-07-05T02:16:44.736823+00:00"},{"alias_kind":"pith_short_16","alias_value":"3AGQYODF7QGVQQ57","created_at":"2026-07-05T02:16:44.736823+00:00"},{"alias_kind":"pith_short_8","alias_value":"3AGQYODF","created_at":"2026-07-05T02:16:44.736823+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.12587","citing_title":"Strategic Decision Support for AI Agents","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09716","citing_title":"Medical Model Synthesis Architectures: A Case Study","ref_index":293,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3AGQYODF7QGVQQ572CSDJBAKLG","json":"https://pith.science/pith/3AGQYODF7QGVQQ572CSDJBAKLG.json","graph_json":"https://pith.science/api/pith-number/3AGQYODF7QGVQQ572CSDJBAKLG/graph.json","events_json":"https://pith.science/api/pith-number/3AGQYODF7QGVQQ572CSDJBAKLG/events.json","paper":"https://pith.science/paper/3AGQYODF"},"agent_actions":{"view_html":"https://pith.science/pith/3AGQYODF7QGVQQ572CSDJBAKLG","download_json":"https://pith.science/pith/3AGQYODF7QGVQQ572CSDJBAKLG.json","view_paper":"https://pith.science/paper/3AGQYODF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2004.13102&json=true","fetch_graph":"https://pith.science/api/pith-number/3AGQYODF7QGVQQ572CSDJBAKLG/graph.json","fetch_events":"https://pith.science/api/pith-number/3AGQYODF7QGVQQ572CSDJBAKLG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3AGQYODF7QGVQQ572CSDJBAKLG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3AGQYODF7QGVQQ572CSDJBAKLG/action/storage_attestation","attest_author":"https://pith.science/pith/3AGQYODF7QGVQQ572CSDJBAKLG/action/author_attestation","sign_citation":"https://pith.science/pith/3AGQYODF7QGVQQ572CSDJBAKLG/action/citation_signature","submit_replication":"https://pith.science/pith/3AGQYODF7QGVQQ572CSDJBAKLG/action/replication_record"}},"created_at":"2026-07-05T02:16:44.736823+00:00","updated_at":"2026-07-05T02:16:44.736823+00:00"}