{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:KNMRUQ3AF3FDX2MAIF4T5PE3RZ","short_pith_number":"pith:KNMRUQ3A","canonical_record":{"source":{"id":"2208.11408","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.HC","submitted_at":"2022-08-24T10:03:54Z","cross_cats_sorted":["cs.AI","cs.CY","cs.LG"],"title_canon_sha256":"489a64036a5128351414e2cd5d70f1cbfd8b4615d79a702957703f8d0fed44d9","abstract_canon_sha256":"538e2f40187ce50f407622e2afb0d9ef833582171b5db6b495778244c1f3ba1e"},"schema_version":"1.0"},"canonical_sha256":"53591a43602eca3be98041793ebc9b8e734cd75b0b1e4a1c87047f2966901dce","source":{"kind":"arxiv","id":"2208.11408","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2208.11408","created_at":"2026-07-05T04:51:14Z"},{"alias_kind":"arxiv_version","alias_value":"2208.11408v1","created_at":"2026-07-05T04:51:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.11408","created_at":"2026-07-05T04:51:14Z"},{"alias_kind":"pith_short_12","alias_value":"KNMRUQ3AF3FD","created_at":"2026-07-05T04:51:14Z"},{"alias_kind":"pith_short_16","alias_value":"KNMRUQ3AF3FDX2MA","created_at":"2026-07-05T04:51:14Z"},{"alias_kind":"pith_short_8","alias_value":"KNMRUQ3A","created_at":"2026-07-05T04:51:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:KNMRUQ3AF3FDX2MAIF4T5PE3RZ","target":"record","payload":{"canonical_record":{"source":{"id":"2208.11408","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.HC","submitted_at":"2022-08-24T10:03:54Z","cross_cats_sorted":["cs.AI","cs.CY","cs.LG"],"title_canon_sha256":"489a64036a5128351414e2cd5d70f1cbfd8b4615d79a702957703f8d0fed44d9","abstract_canon_sha256":"538e2f40187ce50f407622e2afb0d9ef833582171b5db6b495778244c1f3ba1e"},"schema_version":"1.0"},"canonical_sha256":"53591a43602eca3be98041793ebc9b8e734cd75b0b1e4a1c87047f2966901dce","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:51:14.830424Z","signature_b64":"L1mCZuj8MFhRWwpV59o5U9ByGoj5MCTkmkcZ1ERGcA5d0QT9K4DZNmyjjzUf//dt2kKwP4GvbE+laP3uBmkpCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"53591a43602eca3be98041793ebc9b8e734cd75b0b1e4a1c87047f2966901dce","last_reissued_at":"2026-07-05T04:51:14.829961Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:51:14.829961Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2208.11408","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:51:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q7Pj1fz668SYKQAUOTWOJOFxMOC//9CPD0Q/2XpmiSE5DsD4up4rCT7W3mGIy83Kdwm3sEjH+aXs8HdTH8+JAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T15:36:34.382431Z"},"content_sha256":"54491c4f52b0ad36e98a3e19e68c363ce225370c3b12e5edf892857dd0252ea3","schema_version":"1.0","event_id":"sha256:54491c4f52b0ad36e98a3e19e68c363ce225370c3b12e5edf892857dd0252ea3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:KNMRUQ3AF3FDX2MAIF4T5PE3RZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Explainable AI for tailored electricity consumption feedback -- an experimental evaluation of visualizations","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.CY","cs.LG"],"primary_cat":"cs.HC","authors_text":"Felix Haag, Jacqueline Wastensteiner, Konstantin Hopf, Tobias M. Weiss","submitted_at":"2022-08-24T10:03:54Z","abstract_excerpt":"Machine learning (ML) methods can effectively analyse data, recognize patterns in them, and make high-quality predictions. Good predictions usually come along with \"black-box\" models that are unable to present the detected patterns in a human-readable way. Technical developments recently led to eXplainable Artificial Intelligence (XAI) techniques that aim to open such black-boxes and enable humans to gain new insights from detected patterns. We investigated the application of XAI in an area where specific insights can have a significant effect on consumer behaviour, namely electricity use. Kno"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.11408","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/2208.11408/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:51:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ysh5Ol2smUeMIsbDqjii6OYDDzidSxeNNXRiM6ssvTPhXupmyGg43hZOSxxpRN93hW4Bqhic1Cdsrd2UFIEpCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T15:36:34.382991Z"},"content_sha256":"a8c135137cba2b923ac520b20bad0b86800040c41b7d7c0f163071b0b70bff3b","schema_version":"1.0","event_id":"sha256:a8c135137cba2b923ac520b20bad0b86800040c41b7d7c0f163071b0b70bff3b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KNMRUQ3AF3FDX2MAIF4T5PE3RZ/bundle.json","state_url":"https://pith.science/pith/KNMRUQ3AF3FDX2MAIF4T5PE3RZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KNMRUQ3AF3FDX2MAIF4T5PE3RZ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-21T15:36:34Z","links":{"resolver":"https://pith.science/pith/KNMRUQ3AF3FDX2MAIF4T5PE3RZ","bundle":"https://pith.science/pith/KNMRUQ3AF3FDX2MAIF4T5PE3RZ/bundle.json","state":"https://pith.science/pith/KNMRUQ3AF3FDX2MAIF4T5PE3RZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KNMRUQ3AF3FDX2MAIF4T5PE3RZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:KNMRUQ3AF3FDX2MAIF4T5PE3RZ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"538e2f40187ce50f407622e2afb0d9ef833582171b5db6b495778244c1f3ba1e","cross_cats_sorted":["cs.AI","cs.CY","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.HC","submitted_at":"2022-08-24T10:03:54Z","title_canon_sha256":"489a64036a5128351414e2cd5d70f1cbfd8b4615d79a702957703f8d0fed44d9"},"schema_version":"1.0","source":{"id":"2208.11408","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2208.11408","created_at":"2026-07-05T04:51:14Z"},{"alias_kind":"arxiv_version","alias_value":"2208.11408v1","created_at":"2026-07-05T04:51:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.11408","created_at":"2026-07-05T04:51:14Z"},{"alias_kind":"pith_short_12","alias_value":"KNMRUQ3AF3FD","created_at":"2026-07-05T04:51:14Z"},{"alias_kind":"pith_short_16","alias_value":"KNMRUQ3AF3FDX2MA","created_at":"2026-07-05T04:51:14Z"},{"alias_kind":"pith_short_8","alias_value":"KNMRUQ3A","created_at":"2026-07-05T04:51:14Z"}],"graph_snapshots":[{"event_id":"sha256:a8c135137cba2b923ac520b20bad0b86800040c41b7d7c0f163071b0b70bff3b","target":"graph","created_at":"2026-07-05T04:51:14Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2208.11408/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Machine learning (ML) methods can effectively analyse data, recognize patterns in them, and make high-quality predictions. Good predictions usually come along with \"black-box\" models that are unable to present the detected patterns in a human-readable way. Technical developments recently led to eXplainable Artificial Intelligence (XAI) techniques that aim to open such black-boxes and enable humans to gain new insights from detected patterns. We investigated the application of XAI in an area where specific insights can have a significant effect on consumer behaviour, namely electricity use. Kno","authors_text":"Felix Haag, Jacqueline Wastensteiner, Konstantin Hopf, Tobias M. Weiss","cross_cats":["cs.AI","cs.CY","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.HC","submitted_at":"2022-08-24T10:03:54Z","title":"Explainable AI for tailored electricity consumption feedback -- an experimental evaluation of visualizations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.11408","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:54491c4f52b0ad36e98a3e19e68c363ce225370c3b12e5edf892857dd0252ea3","target":"record","created_at":"2026-07-05T04:51:14Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"538e2f40187ce50f407622e2afb0d9ef833582171b5db6b495778244c1f3ba1e","cross_cats_sorted":["cs.AI","cs.CY","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.HC","submitted_at":"2022-08-24T10:03:54Z","title_canon_sha256":"489a64036a5128351414e2cd5d70f1cbfd8b4615d79a702957703f8d0fed44d9"},"schema_version":"1.0","source":{"id":"2208.11408","kind":"arxiv","version":1}},"canonical_sha256":"53591a43602eca3be98041793ebc9b8e734cd75b0b1e4a1c87047f2966901dce","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"53591a43602eca3be98041793ebc9b8e734cd75b0b1e4a1c87047f2966901dce","first_computed_at":"2026-07-05T04:51:14.829961Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:51:14.829961Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"L1mCZuj8MFhRWwpV59o5U9ByGoj5MCTkmkcZ1ERGcA5d0QT9K4DZNmyjjzUf//dt2kKwP4GvbE+laP3uBmkpCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:51:14.830424Z","signed_message":"canonical_sha256_bytes"},"source_id":"2208.11408","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:54491c4f52b0ad36e98a3e19e68c363ce225370c3b12e5edf892857dd0252ea3","sha256:a8c135137cba2b923ac520b20bad0b86800040c41b7d7c0f163071b0b70bff3b"],"state_sha256":"d295806d4f7e669badb64e2c5f6f10fa05636b260296783abe3936a94340997a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OAwnH0st1VwLmmnNAboCewCVmbh/9/wFRAvJEg1XQr23c12hZjZbSCrTgzC9VFrrMb9U93Ce6lI75R68VnjiCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T15:36:34.388531Z","bundle_sha256":"3f2a4277a686553770be5a5886a6ff77a096734cfb3c4ccf4838806503687e4a"}}