{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:XFWMZIIQ5Z3XWWJFF3BZQ47U7S","short_pith_number":"pith:XFWMZIIQ","schema_version":"1.0","canonical_sha256":"b96ccca110ee777b59252ec39873f4fca9cc246bc71d477a2995783abae15258","source":{"kind":"arxiv","id":"2101.07337","version":1},"attestation_state":"computed","paper":{"title":"Dissonance Between Human and Machine Understanding","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Avishek Anand, Jaspreet Singh, Ujwal Gadiraju, Zijian Zhang","submitted_at":"2021-01-18T21:45:35Z","abstract_excerpt":"Complex machine learning models are deployed in several critical domains including healthcare and autonomous vehicles nowadays, albeit as functional black boxes. Consequently, there has been a recent surge in interpreting decisions of such complex models in order to explain their actions to humans. Models that correspond to human interpretation of a task are more desirable in certain contexts and can help attribute liability, build trust, expose biases and in turn build better models. It is, therefore, crucial to understand how and which models conform to human understanding of tasks. In this "},"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":"2101.07337","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2021-01-18T21:45:35Z","cross_cats_sorted":[],"title_canon_sha256":"5bb87ee6f474041a2c623ad7be7ab23acbaa78950d8b438265fe75ccd668b85e","abstract_canon_sha256":"c64f688dab521d0154731a6d48f50cb58b251a9afa11de948f22d632803d5f32"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:07:56.887146Z","signature_b64":"zY4ZwR+sZTjPDMs6c+I22efYscf7HBBrWjOD3mFfGulvfIG+uBK46nyAwa7dbjZ5IJEY3R2reZ1wZ3UpOvWWAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b96ccca110ee777b59252ec39873f4fca9cc246bc71d477a2995783abae15258","last_reissued_at":"2026-07-05T02:07:56.886595Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:07:56.886595Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dissonance Between Human and Machine Understanding","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Avishek Anand, Jaspreet Singh, Ujwal Gadiraju, Zijian Zhang","submitted_at":"2021-01-18T21:45:35Z","abstract_excerpt":"Complex machine learning models are deployed in several critical domains including healthcare and autonomous vehicles nowadays, albeit as functional black boxes. Consequently, there has been a recent surge in interpreting decisions of such complex models in order to explain their actions to humans. Models that correspond to human interpretation of a task are more desirable in certain contexts and can help attribute liability, build trust, expose biases and in turn build better models. It is, therefore, crucial to understand how and which models conform to human understanding of tasks. In this "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.07337","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/2101.07337/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":"2101.07337","created_at":"2026-07-05T02:07:56.886673+00:00"},{"alias_kind":"arxiv_version","alias_value":"2101.07337v1","created_at":"2026-07-05T02:07:56.886673+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.07337","created_at":"2026-07-05T02:07:56.886673+00:00"},{"alias_kind":"pith_short_12","alias_value":"XFWMZIIQ5Z3X","created_at":"2026-07-05T02:07:56.886673+00:00"},{"alias_kind":"pith_short_16","alias_value":"XFWMZIIQ5Z3XWWJF","created_at":"2026-07-05T02:07:56.886673+00:00"},{"alias_kind":"pith_short_8","alias_value":"XFWMZIIQ","created_at":"2026-07-05T02:07:56.886673+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.18968","citing_title":"Perception of Visual Content: Differences Between Humans and Foundation Models","ref_index":38,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XFWMZIIQ5Z3XWWJFF3BZQ47U7S","json":"https://pith.science/pith/XFWMZIIQ5Z3XWWJFF3BZQ47U7S.json","graph_json":"https://pith.science/api/pith-number/XFWMZIIQ5Z3XWWJFF3BZQ47U7S/graph.json","events_json":"https://pith.science/api/pith-number/XFWMZIIQ5Z3XWWJFF3BZQ47U7S/events.json","paper":"https://pith.science/paper/XFWMZIIQ"},"agent_actions":{"view_html":"https://pith.science/pith/XFWMZIIQ5Z3XWWJFF3BZQ47U7S","download_json":"https://pith.science/pith/XFWMZIIQ5Z3XWWJFF3BZQ47U7S.json","view_paper":"https://pith.science/paper/XFWMZIIQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2101.07337&json=true","fetch_graph":"https://pith.science/api/pith-number/XFWMZIIQ5Z3XWWJFF3BZQ47U7S/graph.json","fetch_events":"https://pith.science/api/pith-number/XFWMZIIQ5Z3XWWJFF3BZQ47U7S/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XFWMZIIQ5Z3XWWJFF3BZQ47U7S/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XFWMZIIQ5Z3XWWJFF3BZQ47U7S/action/storage_attestation","attest_author":"https://pith.science/pith/XFWMZIIQ5Z3XWWJFF3BZQ47U7S/action/author_attestation","sign_citation":"https://pith.science/pith/XFWMZIIQ5Z3XWWJFF3BZQ47U7S/action/citation_signature","submit_replication":"https://pith.science/pith/XFWMZIIQ5Z3XWWJFF3BZQ47U7S/action/replication_record"}},"created_at":"2026-07-05T02:07:56.886673+00:00","updated_at":"2026-07-05T02:07:56.886673+00:00"}