{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:2IO6YI5XDCIEZOZIWLQ5DTK63P","short_pith_number":"pith:2IO6YI5X","canonical_record":{"source":{"id":"2302.11137","kind":"arxiv","version":7},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2023-02-22T04:14:09Z","cross_cats_sorted":[],"title_canon_sha256":"912349213e5d9d108b3fa452926e1ea1f51d4a87b65d24c743aff41d8c566556","abstract_canon_sha256":"15f7c3961672725fc38019d9eb719d4636d9c745d63bd4c000e21f8b68a092c3"},"schema_version":"1.0"},"canonical_sha256":"d21dec23b718904cbb28b2e1d1cd5edbc74d60dcd1bb97ee46aef4e35fd42003","source":{"kind":"arxiv","id":"2302.11137","version":7},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.11137","created_at":"2026-07-05T06:48:47Z"},{"alias_kind":"arxiv_version","alias_value":"2302.11137v7","created_at":"2026-07-05T06:48:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.11137","created_at":"2026-07-05T06:48:47Z"},{"alias_kind":"pith_short_12","alias_value":"2IO6YI5XDCIE","created_at":"2026-07-05T06:48:47Z"},{"alias_kind":"pith_short_16","alias_value":"2IO6YI5XDCIEZOZI","created_at":"2026-07-05T06:48:47Z"},{"alias_kind":"pith_short_8","alias_value":"2IO6YI5X","created_at":"2026-07-05T06:48:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:2IO6YI5XDCIEZOZIWLQ5DTK63P","target":"record","payload":{"canonical_record":{"source":{"id":"2302.11137","kind":"arxiv","version":7},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2023-02-22T04:14:09Z","cross_cats_sorted":[],"title_canon_sha256":"912349213e5d9d108b3fa452926e1ea1f51d4a87b65d24c743aff41d8c566556","abstract_canon_sha256":"15f7c3961672725fc38019d9eb719d4636d9c745d63bd4c000e21f8b68a092c3"},"schema_version":"1.0"},"canonical_sha256":"d21dec23b718904cbb28b2e1d1cd5edbc74d60dcd1bb97ee46aef4e35fd42003","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:48:47.245726Z","signature_b64":"aYhRHDKuOOfMYos1kNvLZdal5JAAKUdgULuh6NrpPIpyybl9IeGXhURfKvcR6PrAz+/uukDBQwJ1T698B3RlBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d21dec23b718904cbb28b2e1d1cd5edbc74d60dcd1bb97ee46aef4e35fd42003","last_reissued_at":"2026-07-05T06:48:47.245208Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:48:47.245208Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2302.11137","source_version":7,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:48:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"S0TK2X6fFeqPH8xhCXj9AsKbRqYbOO0X6vS7b1AFHjGu+BIu1fsRvvziRYIu+9ouG86G0QIQ38jkXAoLC0ieAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T07:32:05.669892Z"},"content_sha256":"9b5a27889be5d6c7544b4dbb7fe0af17aa14cba9cd245a3d6631fbeb7c89eb10","schema_version":"1.0","event_id":"sha256:9b5a27889be5d6c7544b4dbb7fe0af17aa14cba9cd245a3d6631fbeb7c89eb10"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:2IO6YI5XDCIEZOZIWLQ5DTK63P","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fairguard: Harness Logic-based Fairness Rules in Smart Cities","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Ayan Mukhopadhyay, Meiyi Ma, Xuqing Gao, Yiqi Zhao, Ziyan An","submitted_at":"2023-02-22T04:14:09Z","abstract_excerpt":"Smart cities operate on computational predictive frameworks that collect, aggregate, and utilize data from large-scale sensor networks. However, these frameworks are prone to multiple sources of data and algorithmic bias, which often lead to unfair prediction results. In this work, we first demonstrate that bias persists at a micro-level both temporally and spatially by studying real city data from Chattanooga, TN. To alleviate the issue of such bias, we introduce Fairguard, a micro-level temporal logic-based approach for fair smart city policy adjustment and generation in complex temporal-spa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.11137","kind":"arxiv","version":7},"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/2302.11137/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":"2026-07-05T06:48:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"C1OCB2NbWFvTaeZkWE/Kn3dx5TsLgbeKELPESl4L8kF8/4snz+60tKvyyWbcAA5l5WdCc+GBllOpLpBq+fNQDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T07:32:05.670563Z"},"content_sha256":"95c2775007f1786af8c767d31b01b49584c91b6593757c469ff693545dd4c737","schema_version":"1.0","event_id":"sha256:95c2775007f1786af8c767d31b01b49584c91b6593757c469ff693545dd4c737"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2IO6YI5XDCIEZOZIWLQ5DTK63P/bundle.json","state_url":"https://pith.science/pith/2IO6YI5XDCIEZOZIWLQ5DTK63P/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2IO6YI5XDCIEZOZIWLQ5DTK63P/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":"2026-07-07T07:32:05Z","links":{"resolver":"https://pith.science/pith/2IO6YI5XDCIEZOZIWLQ5DTK63P","bundle":"https://pith.science/pith/2IO6YI5XDCIEZOZIWLQ5DTK63P/bundle.json","state":"https://pith.science/pith/2IO6YI5XDCIEZOZIWLQ5DTK63P/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2IO6YI5XDCIEZOZIWLQ5DTK63P/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:2IO6YI5XDCIEZOZIWLQ5DTK63P","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":"15f7c3961672725fc38019d9eb719d4636d9c745d63bd4c000e21f8b68a092c3","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2023-02-22T04:14:09Z","title_canon_sha256":"912349213e5d9d108b3fa452926e1ea1f51d4a87b65d24c743aff41d8c566556"},"schema_version":"1.0","source":{"id":"2302.11137","kind":"arxiv","version":7}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.11137","created_at":"2026-07-05T06:48:47Z"},{"alias_kind":"arxiv_version","alias_value":"2302.11137v7","created_at":"2026-07-05T06:48:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.11137","created_at":"2026-07-05T06:48:47Z"},{"alias_kind":"pith_short_12","alias_value":"2IO6YI5XDCIE","created_at":"2026-07-05T06:48:47Z"},{"alias_kind":"pith_short_16","alias_value":"2IO6YI5XDCIEZOZI","created_at":"2026-07-05T06:48:47Z"},{"alias_kind":"pith_short_8","alias_value":"2IO6YI5X","created_at":"2026-07-05T06:48:47Z"}],"graph_snapshots":[{"event_id":"sha256:95c2775007f1786af8c767d31b01b49584c91b6593757c469ff693545dd4c737","target":"graph","created_at":"2026-07-05T06:48:47Z","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/2302.11137/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Smart cities operate on computational predictive frameworks that collect, aggregate, and utilize data from large-scale sensor networks. However, these frameworks are prone to multiple sources of data and algorithmic bias, which often lead to unfair prediction results. In this work, we first demonstrate that bias persists at a micro-level both temporally and spatially by studying real city data from Chattanooga, TN. To alleviate the issue of such bias, we introduce Fairguard, a micro-level temporal logic-based approach for fair smart city policy adjustment and generation in complex temporal-spa","authors_text":"Ayan Mukhopadhyay, Meiyi Ma, Xuqing Gao, Yiqi Zhao, Ziyan An","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2023-02-22T04:14:09Z","title":"Fairguard: Harness Logic-based Fairness Rules in Smart Cities"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.11137","kind":"arxiv","version":7},"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":"sha256:9b5a27889be5d6c7544b4dbb7fe0af17aa14cba9cd245a3d6631fbeb7c89eb10","target":"record","created_at":"2026-07-05T06:48:47Z","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":"15f7c3961672725fc38019d9eb719d4636d9c745d63bd4c000e21f8b68a092c3","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2023-02-22T04:14:09Z","title_canon_sha256":"912349213e5d9d108b3fa452926e1ea1f51d4a87b65d24c743aff41d8c566556"},"schema_version":"1.0","source":{"id":"2302.11137","kind":"arxiv","version":7}},"canonical_sha256":"d21dec23b718904cbb28b2e1d1cd5edbc74d60dcd1bb97ee46aef4e35fd42003","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d21dec23b718904cbb28b2e1d1cd5edbc74d60dcd1bb97ee46aef4e35fd42003","first_computed_at":"2026-07-05T06:48:47.245208Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:48:47.245208Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"aYhRHDKuOOfMYos1kNvLZdal5JAAKUdgULuh6NrpPIpyybl9IeGXhURfKvcR6PrAz+/uukDBQwJ1T698B3RlBw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:48:47.245726Z","signed_message":"canonical_sha256_bytes"},"source_id":"2302.11137","source_kind":"arxiv","source_version":7}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9b5a27889be5d6c7544b4dbb7fe0af17aa14cba9cd245a3d6631fbeb7c89eb10","sha256:95c2775007f1786af8c767d31b01b49584c91b6593757c469ff693545dd4c737"],"state_sha256":"af7dc65c1129f0a1ef112dd0c1c4f7a74d462e3f1aa3751f810dd24c9b6b3813"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Areld2F02zazLKxTkpOq++MR8ytH8pUO/Ig1EZJnAUO7l1ANeRn4T+QSSOohoLKNjoBgOhWGVXI4+aOOIbeGDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T07:32:05.675074Z","bundle_sha256":"2cf28640715e984e7ed81ff5f9f175c7b3381b99781b841c244522baae046b6c"}}