{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:L267XQIY5IRSTPBZNI67YHRKRU","short_pith_number":"pith:L267XQIY","canonical_record":{"source":{"id":"2109.09946","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2021-09-21T04:05:12Z","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"title_canon_sha256":"3dcc19dd086b88637749bbd7baca0aa440c3631f9d6e339b08943aa7299ba530","abstract_canon_sha256":"61e164ad4c370dc258e83e81a84a0f0dad9317876d0d1663998a4f7387cfb97a"},"schema_version":"1.0"},"canonical_sha256":"5ebdfbc118ea2329bc396a3dfc1e2a8d126af1ecae8a483db631bd696e9fe279","source":{"kind":"arxiv","id":"2109.09946","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.09946","created_at":"2026-07-05T03:15:56Z"},{"alias_kind":"arxiv_version","alias_value":"2109.09946v1","created_at":"2026-07-05T03:15:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.09946","created_at":"2026-07-05T03:15:56Z"},{"alias_kind":"pith_short_12","alias_value":"L267XQIY5IRS","created_at":"2026-07-05T03:15:56Z"},{"alias_kind":"pith_short_16","alias_value":"L267XQIY5IRSTPBZ","created_at":"2026-07-05T03:15:56Z"},{"alias_kind":"pith_short_8","alias_value":"L267XQIY","created_at":"2026-07-05T03:15:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:L267XQIY5IRSTPBZNI67YHRKRU","target":"record","payload":{"canonical_record":{"source":{"id":"2109.09946","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2021-09-21T04:05:12Z","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"title_canon_sha256":"3dcc19dd086b88637749bbd7baca0aa440c3631f9d6e339b08943aa7299ba530","abstract_canon_sha256":"61e164ad4c370dc258e83e81a84a0f0dad9317876d0d1663998a4f7387cfb97a"},"schema_version":"1.0"},"canonical_sha256":"5ebdfbc118ea2329bc396a3dfc1e2a8d126af1ecae8a483db631bd696e9fe279","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:15:56.265424Z","signature_b64":"7FEMEqYKjkfxQGVQOv+NWdL2uKwxBTKM1FeYgnj0ZCxcsvfZOF4lRyxCK2WBz0T9zvamKokQhKUdZe8sX35eDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5ebdfbc118ea2329bc396a3dfc1e2a8d126af1ecae8a483db631bd696e9fe279","last_reissued_at":"2026-07-05T03:15:56.264996Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:15:56.264996Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2109.09946","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":"2026-07-05T03:15:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sAAj6mI/FVaT7q5DLhC6RZQBrKWyuAo2nuBE/WGOi2D+UIJGbkT5FFMcdd/+BCJawW+CsBM7Fn+tNgyZsVLDCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T02:40:13.040293Z"},"content_sha256":"e8eb3be150cd88621c54b37a47c1f6e0dd23aa1fc2fac59d33cfe7686cb00f70","schema_version":"1.0","event_id":"sha256:e8eb3be150cd88621c54b37a47c1f6e0dd23aa1fc2fac59d33cfe7686cb00f70"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:L267XQIY5IRSTPBZNI67YHRKRU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Identifying biases in legal data: An algorithmic fairness perspective","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","stat.ML"],"primary_cat":"cs.CY","authors_text":"Jackson Sargent, Melanie Weber","submitted_at":"2021-09-21T04:05:12Z","abstract_excerpt":"The need to address representation biases and sentencing disparities in legal case data has long been recognized. Here, we study the problem of identifying and measuring biases in large-scale legal case data from an algorithmic fairness perspective. Our approach utilizes two regression models: A baseline that represents the decisions of a \"typical\" judge as given by the data and a \"fair\" judge that applies one of three fairness concepts. Comparing the decisions of the \"typical\" judge and the \"fair\" judge allows for quantifying biases across demographic groups, as we demonstrate in four case st"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.09946","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/2109.09946/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":"2026-07-05T03:15:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rhyx8Z2nbPWyyLzpwaQDRMoa/ljvxPttXHWEj5Gl3vHsHt5LXI/+7h88Tsl1PmtFZeIczHEBcwRBpl9p0x8RCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T02:40:13.041040Z"},"content_sha256":"9df23b8f453d70f74b8206e93dccc44e1a2346c5be9ae44b5ac01a4f941df249","schema_version":"1.0","event_id":"sha256:9df23b8f453d70f74b8206e93dccc44e1a2346c5be9ae44b5ac01a4f941df249"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/L267XQIY5IRSTPBZNI67YHRKRU/bundle.json","state_url":"https://pith.science/pith/L267XQIY5IRSTPBZNI67YHRKRU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/L267XQIY5IRSTPBZNI67YHRKRU/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":"2026-08-15T02:40:13Z","links":{"resolver":"https://pith.science/pith/L267XQIY5IRSTPBZNI67YHRKRU","bundle":"https://pith.science/pith/L267XQIY5IRSTPBZNI67YHRKRU/bundle.json","state":"https://pith.science/pith/L267XQIY5IRSTPBZNI67YHRKRU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/L267XQIY5IRSTPBZNI67YHRKRU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:L267XQIY5IRSTPBZNI67YHRKRU","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":"61e164ad4c370dc258e83e81a84a0f0dad9317876d0d1663998a4f7387cfb97a","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2021-09-21T04:05:12Z","title_canon_sha256":"3dcc19dd086b88637749bbd7baca0aa440c3631f9d6e339b08943aa7299ba530"},"schema_version":"1.0","source":{"id":"2109.09946","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.09946","created_at":"2026-07-05T03:15:56Z"},{"alias_kind":"arxiv_version","alias_value":"2109.09946v1","created_at":"2026-07-05T03:15:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.09946","created_at":"2026-07-05T03:15:56Z"},{"alias_kind":"pith_short_12","alias_value":"L267XQIY5IRS","created_at":"2026-07-05T03:15:56Z"},{"alias_kind":"pith_short_16","alias_value":"L267XQIY5IRSTPBZ","created_at":"2026-07-05T03:15:56Z"},{"alias_kind":"pith_short_8","alias_value":"L267XQIY","created_at":"2026-07-05T03:15:56Z"}],"graph_snapshots":[{"event_id":"sha256:9df23b8f453d70f74b8206e93dccc44e1a2346c5be9ae44b5ac01a4f941df249","target":"graph","created_at":"2026-07-05T03:15:56Z","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/2109.09946/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The need to address representation biases and sentencing disparities in legal case data has long been recognized. Here, we study the problem of identifying and measuring biases in large-scale legal case data from an algorithmic fairness perspective. Our approach utilizes two regression models: A baseline that represents the decisions of a \"typical\" judge as given by the data and a \"fair\" judge that applies one of three fairness concepts. Comparing the decisions of the \"typical\" judge and the \"fair\" judge allows for quantifying biases across demographic groups, as we demonstrate in four case st","authors_text":"Jackson Sargent, Melanie Weber","cross_cats":["cs.AI","cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2021-09-21T04:05:12Z","title":"Identifying biases in legal data: An algorithmic fairness perspective"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.09946","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:e8eb3be150cd88621c54b37a47c1f6e0dd23aa1fc2fac59d33cfe7686cb00f70","target":"record","created_at":"2026-07-05T03:15:56Z","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":"61e164ad4c370dc258e83e81a84a0f0dad9317876d0d1663998a4f7387cfb97a","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2021-09-21T04:05:12Z","title_canon_sha256":"3dcc19dd086b88637749bbd7baca0aa440c3631f9d6e339b08943aa7299ba530"},"schema_version":"1.0","source":{"id":"2109.09946","kind":"arxiv","version":1}},"canonical_sha256":"5ebdfbc118ea2329bc396a3dfc1e2a8d126af1ecae8a483db631bd696e9fe279","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5ebdfbc118ea2329bc396a3dfc1e2a8d126af1ecae8a483db631bd696e9fe279","first_computed_at":"2026-07-05T03:15:56.264996Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:15:56.264996Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7FEMEqYKjkfxQGVQOv+NWdL2uKwxBTKM1FeYgnj0ZCxcsvfZOF4lRyxCK2WBz0T9zvamKokQhKUdZe8sX35eDg==","signature_status":"signed_v1","signed_at":"2026-07-05T03:15:56.265424Z","signed_message":"canonical_sha256_bytes"},"source_id":"2109.09946","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e8eb3be150cd88621c54b37a47c1f6e0dd23aa1fc2fac59d33cfe7686cb00f70","sha256:9df23b8f453d70f74b8206e93dccc44e1a2346c5be9ae44b5ac01a4f941df249"],"state_sha256":"9976ce3fe6eef937cfe4a9eeed9625214a2beece35dd075481eab8f1cd44455f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Nc8um6MejohiitM77D0UXiJLseV2o7RRf90TYpKjbH5W4zCY6NcErsE7BktvzTg2SvxwKBngR7Z9AB8ZnxX/Cg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T02:40:13.047141Z","bundle_sha256":"58ab10b3bd795c8561e76f711f5c896c906c19ee35b97fc6c9d3061ffb1258bc"}}