{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:MMNZJIM4QUSF5ZRSDFDX3FNBRH","short_pith_number":"pith:MMNZJIM4","schema_version":"1.0","canonical_sha256":"631b94a19c85245ee63219477d95a189d3211a96a69a6b61eeb9c473d77dd273","source":{"kind":"arxiv","id":"2009.09723","version":1},"attestation_state":"computed","paper":{"title":"Machine Guides, Human Supervises: Interactive Learning with Global Explanations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Mohit Kumar, Stefano Teso, Teodora Popordanoska","submitted_at":"2020-09-21T09:55:30Z","abstract_excerpt":"We introduce explanatory guided learning (XGL), a novel interactive learning strategy in which a machine guides a human supervisor toward selecting informative examples for a classifier. The guidance is provided by means of global explanations, which summarize the classifier's behavior on different regions of the instance space and expose its flaws. Compared to other explanatory interactive learning strategies, which are machine-initiated and rely on local explanations, XGL is designed to be robust against cases in which the explanations supplied by the machine oversell the classifier's qualit"},"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":"2009.09723","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-09-21T09:55:30Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"e72c77209165a7e956407aae0785a10026da475a4c3da18bee42fdcff81cfef3","abstract_canon_sha256":"1a04198a3d435558476d83faa3baa2c939d49d6cb0570aeeba881d933907fcad"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:36:52.417305Z","signature_b64":"Z3Xbe4FaSkMteld1ii5UgvTIj8ghNTmWx3XHC0aJ591WfAF4UQ1seYl/OGE2Ba80Fr3FbvL/MPA+yXlMwwXKDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"631b94a19c85245ee63219477d95a189d3211a96a69a6b61eeb9c473d77dd273","last_reissued_at":"2026-07-05T01:36:52.416924Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:36:52.416924Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Machine Guides, Human Supervises: Interactive Learning with Global Explanations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Mohit Kumar, Stefano Teso, Teodora Popordanoska","submitted_at":"2020-09-21T09:55:30Z","abstract_excerpt":"We introduce explanatory guided learning (XGL), a novel interactive learning strategy in which a machine guides a human supervisor toward selecting informative examples for a classifier. The guidance is provided by means of global explanations, which summarize the classifier's behavior on different regions of the instance space and expose its flaws. Compared to other explanatory interactive learning strategies, which are machine-initiated and rely on local explanations, XGL is designed to be robust against cases in which the explanations supplied by the machine oversell the classifier's qualit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.09723","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/2009.09723/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":"2009.09723","created_at":"2026-07-05T01:36:52.416981+00:00"},{"alias_kind":"arxiv_version","alias_value":"2009.09723v1","created_at":"2026-07-05T01:36:52.416981+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.09723","created_at":"2026-07-05T01:36:52.416981+00:00"},{"alias_kind":"pith_short_12","alias_value":"MMNZJIM4QUSF","created_at":"2026-07-05T01:36:52.416981+00:00"},{"alias_kind":"pith_short_16","alias_value":"MMNZJIM4QUSF5ZRS","created_at":"2026-07-05T01:36:52.416981+00:00"},{"alias_kind":"pith_short_8","alias_value":"MMNZJIM4","created_at":"2026-07-05T01:36:52.416981+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2405.10729","citing_title":"Contestable AI needs Computational Argumentation","ref_index":84,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MMNZJIM4QUSF5ZRSDFDX3FNBRH","json":"https://pith.science/pith/MMNZJIM4QUSF5ZRSDFDX3FNBRH.json","graph_json":"https://pith.science/api/pith-number/MMNZJIM4QUSF5ZRSDFDX3FNBRH/graph.json","events_json":"https://pith.science/api/pith-number/MMNZJIM4QUSF5ZRSDFDX3FNBRH/events.json","paper":"https://pith.science/paper/MMNZJIM4"},"agent_actions":{"view_html":"https://pith.science/pith/MMNZJIM4QUSF5ZRSDFDX3FNBRH","download_json":"https://pith.science/pith/MMNZJIM4QUSF5ZRSDFDX3FNBRH.json","view_paper":"https://pith.science/paper/MMNZJIM4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2009.09723&json=true","fetch_graph":"https://pith.science/api/pith-number/MMNZJIM4QUSF5ZRSDFDX3FNBRH/graph.json","fetch_events":"https://pith.science/api/pith-number/MMNZJIM4QUSF5ZRSDFDX3FNBRH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MMNZJIM4QUSF5ZRSDFDX3FNBRH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MMNZJIM4QUSF5ZRSDFDX3FNBRH/action/storage_attestation","attest_author":"https://pith.science/pith/MMNZJIM4QUSF5ZRSDFDX3FNBRH/action/author_attestation","sign_citation":"https://pith.science/pith/MMNZJIM4QUSF5ZRSDFDX3FNBRH/action/citation_signature","submit_replication":"https://pith.science/pith/MMNZJIM4QUSF5ZRSDFDX3FNBRH/action/replication_record"}},"created_at":"2026-07-05T01:36:52.416981+00:00","updated_at":"2026-07-05T01:36:52.416981+00:00"}