{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:F2WVFUIZ653EX5XMORRBTH55AF","short_pith_number":"pith:F2WVFUIZ","schema_version":"1.0","canonical_sha256":"2ead52d119f7764bf6ec7462199fbd0140c68e6b719607eac12e2e5b0629a2eb","source":{"kind":"arxiv","id":"1805.04508","version":1},"attestation_state":"computed","paper":{"title":"Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Saif M. Mohammad, Svetlana Kiritchenko","submitted_at":"2018-05-11T17:57:40Z","abstract_excerpt":"Automatic machine learning systems can inadvertently accentuate and perpetuate inappropriate human biases. Past work on examining inappropriate biases has largely focused on just individual systems. Further, there is no benchmark dataset for examining inappropriate biases in systems. Here for the first time, we present the Equity Evaluation Corpus (EEC), which consists of 8,640 English sentences carefully chosen to tease out biases towards certain races and genders. We use the dataset to examine 219 automatic sentiment analysis systems that took part in a recent shared task, SemEval-2018 Task "},"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":"1805.04508","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-05-11T17:57:40Z","cross_cats_sorted":[],"title_canon_sha256":"b1fa9b991663fc2ddf3b29d7f492f3909d3c925ddb2768abdd3a1e8800ef7e94","abstract_canon_sha256":"ed57a5d5001f92e965657d59f55c7097f68b41519c14c5ef7d6af1e762b026ea"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:16:10.086724Z","signature_b64":"72Zoxgr8k3APCwwzyFPmTBqJx/WvbaJWWPZMMTUPywuIYGU+ZivkC1CGJtIJS2r8FNkmUq937HjpFRgzlLyxCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2ead52d119f7764bf6ec7462199fbd0140c68e6b719607eac12e2e5b0629a2eb","last_reissued_at":"2026-05-18T00:16:10.086160Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:16:10.086160Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Saif M. Mohammad, Svetlana Kiritchenko","submitted_at":"2018-05-11T17:57:40Z","abstract_excerpt":"Automatic machine learning systems can inadvertently accentuate and perpetuate inappropriate human biases. Past work on examining inappropriate biases has largely focused on just individual systems. Further, there is no benchmark dataset for examining inappropriate biases in systems. Here for the first time, we present the Equity Evaluation Corpus (EEC), which consists of 8,640 English sentences carefully chosen to tease out biases towards certain races and genders. We use the dataset to examine 219 automatic sentiment analysis systems that took part in a recent shared task, SemEval-2018 Task "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.04508","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":"1805.04508","created_at":"2026-05-18T00:16:10.086245+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.04508v1","created_at":"2026-05-18T00:16:10.086245+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.04508","created_at":"2026-05-18T00:16:10.086245+00:00"},{"alias_kind":"pith_short_12","alias_value":"F2WVFUIZ653E","created_at":"2026-05-18T12:32:22.470017+00:00"},{"alias_kind":"pith_short_16","alias_value":"F2WVFUIZ653EX5XM","created_at":"2026-05-18T12:32:22.470017+00:00"},{"alias_kind":"pith_short_8","alias_value":"F2WVFUIZ","created_at":"2026-05-18T12:32:22.470017+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.14672","citing_title":"SPAGBias: Uncovering and Tracing Structured Spatial Gender Bias in Large Language Models","ref_index":32,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/F2WVFUIZ653EX5XMORRBTH55AF","json":"https://pith.science/pith/F2WVFUIZ653EX5XMORRBTH55AF.json","graph_json":"https://pith.science/api/pith-number/F2WVFUIZ653EX5XMORRBTH55AF/graph.json","events_json":"https://pith.science/api/pith-number/F2WVFUIZ653EX5XMORRBTH55AF/events.json","paper":"https://pith.science/paper/F2WVFUIZ"},"agent_actions":{"view_html":"https://pith.science/pith/F2WVFUIZ653EX5XMORRBTH55AF","download_json":"https://pith.science/pith/F2WVFUIZ653EX5XMORRBTH55AF.json","view_paper":"https://pith.science/paper/F2WVFUIZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.04508&json=true","fetch_graph":"https://pith.science/api/pith-number/F2WVFUIZ653EX5XMORRBTH55AF/graph.json","fetch_events":"https://pith.science/api/pith-number/F2WVFUIZ653EX5XMORRBTH55AF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/F2WVFUIZ653EX5XMORRBTH55AF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/F2WVFUIZ653EX5XMORRBTH55AF/action/storage_attestation","attest_author":"https://pith.science/pith/F2WVFUIZ653EX5XMORRBTH55AF/action/author_attestation","sign_citation":"https://pith.science/pith/F2WVFUIZ653EX5XMORRBTH55AF/action/citation_signature","submit_replication":"https://pith.science/pith/F2WVFUIZ653EX5XMORRBTH55AF/action/replication_record"}},"created_at":"2026-05-18T00:16:10.086245+00:00","updated_at":"2026-05-18T00:16:10.086245+00:00"}