{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:BOYSSLJWSBXDM27VD64WSJLIVL","short_pith_number":"pith:BOYSSLJW","schema_version":"1.0","canonical_sha256":"0bb1292d36906e366bf51fb9692568aacf657dbee2ede142c5768ccff3f0d2a6","source":{"kind":"arxiv","id":"2312.00554","version":1},"attestation_state":"computed","paper":{"title":"Questioning Biases in Case Judgment Summaries: Legal Datasets or Large Language Models?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Aniket Deroy, Subhankar Maity","submitted_at":"2023-12-01T13:00:45Z","abstract_excerpt":"The evolution of legal datasets and the advent of large language models (LLMs) have significantly transformed the legal field, particularly in the generation of case judgment summaries. However, a critical concern arises regarding the potential biases embedded within these summaries. This study scrutinizes the biases present in case judgment summaries produced by legal datasets and large language models. The research aims to analyze the impact of biases on legal decision making. By interrogating the accuracy, fairness, and implications of biases in these summaries, this study contributes to a "},"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":"2312.00554","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-12-01T13:00:45Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d92d40183736f3befdcbf5cb51372884df646b4c4e854830a096a4a0c7b209f8","abstract_canon_sha256":"94659b34c7f16b520518a202384cc093e6b5bfd223d78b3572ded1724d9397ed"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:19:11.274018Z","signature_b64":"UWxYpF5DmmHteheuJyaPNQhMeGM5YNGKePjaY4diOjo9dcFu7fc3FqopsybOxd0LHzL/k98wvT0Z+xbKpVBQBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0bb1292d36906e366bf51fb9692568aacf657dbee2ede142c5768ccff3f0d2a6","last_reissued_at":"2026-07-05T07:19:11.273499Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:19:11.273499Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Questioning Biases in Case Judgment Summaries: Legal Datasets or Large Language Models?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Aniket Deroy, Subhankar Maity","submitted_at":"2023-12-01T13:00:45Z","abstract_excerpt":"The evolution of legal datasets and the advent of large language models (LLMs) have significantly transformed the legal field, particularly in the generation of case judgment summaries. However, a critical concern arises regarding the potential biases embedded within these summaries. This study scrutinizes the biases present in case judgment summaries produced by legal datasets and large language models. The research aims to analyze the impact of biases on legal decision making. By interrogating the accuracy, fairness, and implications of biases in these summaries, this study contributes to a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.00554","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/2312.00554/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":"2312.00554","created_at":"2026-07-05T07:19:11.273555+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.00554v1","created_at":"2026-07-05T07:19:11.273555+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.00554","created_at":"2026-07-05T07:19:11.273555+00:00"},{"alias_kind":"pith_short_12","alias_value":"BOYSSLJWSBXD","created_at":"2026-07-05T07:19:11.273555+00:00"},{"alias_kind":"pith_short_16","alias_value":"BOYSSLJWSBXDM27V","created_at":"2026-07-05T07:19:11.273555+00:00"},{"alias_kind":"pith_short_8","alias_value":"BOYSSLJW","created_at":"2026-07-05T07:19:11.273555+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.28044","citing_title":"A Tree-of-Thoughts Inspired Hybrid Approach for Legal Case Judgement Summarization using LLMs","ref_index":229,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BOYSSLJWSBXDM27VD64WSJLIVL","json":"https://pith.science/pith/BOYSSLJWSBXDM27VD64WSJLIVL.json","graph_json":"https://pith.science/api/pith-number/BOYSSLJWSBXDM27VD64WSJLIVL/graph.json","events_json":"https://pith.science/api/pith-number/BOYSSLJWSBXDM27VD64WSJLIVL/events.json","paper":"https://pith.science/paper/BOYSSLJW"},"agent_actions":{"view_html":"https://pith.science/pith/BOYSSLJWSBXDM27VD64WSJLIVL","download_json":"https://pith.science/pith/BOYSSLJWSBXDM27VD64WSJLIVL.json","view_paper":"https://pith.science/paper/BOYSSLJW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.00554&json=true","fetch_graph":"https://pith.science/api/pith-number/BOYSSLJWSBXDM27VD64WSJLIVL/graph.json","fetch_events":"https://pith.science/api/pith-number/BOYSSLJWSBXDM27VD64WSJLIVL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BOYSSLJWSBXDM27VD64WSJLIVL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BOYSSLJWSBXDM27VD64WSJLIVL/action/storage_attestation","attest_author":"https://pith.science/pith/BOYSSLJWSBXDM27VD64WSJLIVL/action/author_attestation","sign_citation":"https://pith.science/pith/BOYSSLJWSBXDM27VD64WSJLIVL/action/citation_signature","submit_replication":"https://pith.science/pith/BOYSSLJWSBXDM27VD64WSJLIVL/action/replication_record"}},"created_at":"2026-07-05T07:19:11.273555+00:00","updated_at":"2026-07-05T07:19:11.273555+00:00"}