{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:BJAQSGECFIKLXOL74IP33TCGOH","short_pith_number":"pith:BJAQSGEC","schema_version":"1.0","canonical_sha256":"0a410918822a14bbb97fe21fbdcc4671c09f30bb0153adeb20ae8ca8d427c76c","source":{"kind":"arxiv","id":"1811.01439","version":1},"attestation_state":"computed","paper":{"title":"Explaining Explanations in AI","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Brent Mittelstadt, Chris Russell, Sandra Wachter","submitted_at":"2018-11-04T21:35:16Z","abstract_excerpt":"Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it's important to remember Box's maxim that \"All models are wrong but some are useful.\" We focus on the distinction between these models and explanations in philosophy and sociology. These models c"},"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":"1811.01439","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-11-04T21:35:16Z","cross_cats_sorted":[],"title_canon_sha256":"2f5cd845e2c48ea2f0a1249455eb06cb34a1ab090958d4a2c978e76d3e8a2546","abstract_canon_sha256":"55c6fd10d38a323d9b61e01b4520d49ee214f19ce30c125b45e9141e4b7591f3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:34.318271Z","signature_b64":"deZXCkq7aQ60wGR/bI6ALI+pD9Xwq1Hvww4x5miCgTDo4SkzEilgcOIDaQEiXHZu5vPT99YVoWMnWBN/iLquAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0a410918822a14bbb97fe21fbdcc4671c09f30bb0153adeb20ae8ca8d427c76c","last_reissued_at":"2026-05-18T00:01:34.317624Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:34.317624Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Explaining Explanations in AI","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Brent Mittelstadt, Chris Russell, Sandra Wachter","submitted_at":"2018-11-04T21:35:16Z","abstract_excerpt":"Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it's important to remember Box's maxim that \"All models are wrong but some are useful.\" We focus on the distinction between these models and explanations in philosophy and sociology. These models c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.01439","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":""},"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":"1811.01439","created_at":"2026-05-18T00:01:34.317718+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.01439v1","created_at":"2026-05-18T00:01:34.317718+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.01439","created_at":"2026-05-18T00:01:34.317718+00:00"},{"alias_kind":"pith_short_12","alias_value":"BJAQSGECFIKL","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_16","alias_value":"BJAQSGECFIKLXOL7","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_8","alias_value":"BJAQSGEC","created_at":"2026-05-18T12:32:16.446611+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"1907.00570","citing_title":"Do Transformer Attention Heads Provide Transparency in Abstractive Summarization?","ref_index":14,"is_internal_anchor":true},{"citing_arxiv_id":"1907.03039","citing_title":"Global Aggregations of Local Explanations for Black Box models","ref_index":10,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BJAQSGECFIKLXOL74IP33TCGOH","json":"https://pith.science/pith/BJAQSGECFIKLXOL74IP33TCGOH.json","graph_json":"https://pith.science/api/pith-number/BJAQSGECFIKLXOL74IP33TCGOH/graph.json","events_json":"https://pith.science/api/pith-number/BJAQSGECFIKLXOL74IP33TCGOH/events.json","paper":"https://pith.science/paper/BJAQSGEC"},"agent_actions":{"view_html":"https://pith.science/pith/BJAQSGECFIKLXOL74IP33TCGOH","download_json":"https://pith.science/pith/BJAQSGECFIKLXOL74IP33TCGOH.json","view_paper":"https://pith.science/paper/BJAQSGEC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.01439&json=true","fetch_graph":"https://pith.science/api/pith-number/BJAQSGECFIKLXOL74IP33TCGOH/graph.json","fetch_events":"https://pith.science/api/pith-number/BJAQSGECFIKLXOL74IP33TCGOH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BJAQSGECFIKLXOL74IP33TCGOH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BJAQSGECFIKLXOL74IP33TCGOH/action/storage_attestation","attest_author":"https://pith.science/pith/BJAQSGECFIKLXOL74IP33TCGOH/action/author_attestation","sign_citation":"https://pith.science/pith/BJAQSGECFIKLXOL74IP33TCGOH/action/citation_signature","submit_replication":"https://pith.science/pith/BJAQSGECFIKLXOL74IP33TCGOH/action/replication_record"}},"created_at":"2026-05-18T00:01:34.317718+00:00","updated_at":"2026-05-18T00:01:34.317718+00:00"}